Bump version to 0.1.9

- Add global _health_check_client variable for singleton pattern
- Define get_or_create_health_check_client() function for health checks
- Add cleanup for health check client in perform_cleanup()

Fixes Bedesten health check error: "name 'get_or_create_health_check_client' is not defined"

.serena/.gitignore

@@ -0,0 +1 @@
+/cache

.serena/project.yml

@@ -0,0 +1,84 @@
+# list of languages for which language servers are started; choose from:
+#   al               bash             clojure          cpp              csharp           csharp_omnisharp
+#   dart             elixir           elm              erlang           fortran          go
+#   haskell          java             julia            kotlin           lua              markdown
+#   nix              perl             php              python           python_jedi      r
+#   rego             ruby             ruby_solargraph  rust             scala            swift
+#   terraform        typescript       typescript_vts   yaml             zig
+# Note:
+#   - For C, use cpp
+#   - For JavaScript, use typescript
+# Special requirements:
+#   - csharp: Requires the presence of a .sln file in the project folder.
+# When using multiple languages, the first language server that supports a given file will be used for that file.
+# The first language is the default language and the respective language server will be used as a fallback.
+# Note that when using the JetBrains backend, language servers are not used and this list is correspondingly ignored.
+languages:
+- python
+
+# the encoding used by text files in the project
+# For a list of possible encodings, see https://docs.python.org/3.11/library/codecs.html#standard-encodings
+encoding: "utf-8"
+
+# whether to use the project's gitignore file to ignore files
+# Added on 2025-04-07
+ignore_all_files_in_gitignore: true
+
+# list of additional paths to ignore
+# same syntax as gitignore, so you can use * and **
+# Was previously called `ignored_dirs`, please update your config if you are using that.
+# Added (renamed) on 2025-04-07
+ignored_paths: []
+
+# whether the project is in read-only mode
+# If set to true, all editing tools will be disabled and attempts to use them will result in an error
+# Added on 2025-04-18
+read_only: false
+
+# list of tool names to exclude. We recommend not excluding any tools, see the readme for more details.
+# Below is the complete list of tools for convenience.
+# To make sure you have the latest list of tools, and to view their descriptions, 
+# execute `uv run scripts/print_tool_overview.py`.
+#
+#  * `activate_project`: Activates a project by name.
+#  * `check_onboarding_performed`: Checks whether project onboarding was already performed.
+#  * `create_text_file`: Creates/overwrites a file in the project directory.
+#  * `delete_lines`: Deletes a range of lines within a file.
+#  * `delete_memory`: Deletes a memory from Serena's project-specific memory store.
+#  * `execute_shell_command`: Executes a shell command.
+#  * `find_referencing_code_snippets`: Finds code snippets in which the symbol at the given location is referenced.
+#  * `find_referencing_symbols`: Finds symbols that reference the symbol at the given location (optionally filtered by type).
+#  * `find_symbol`: Performs a global (or local) search for symbols with/containing a given name/substring (optionally filtered by type).
+#  * `get_current_config`: Prints the current configuration of the agent, including the active and available projects, tools, contexts, and modes.
+#  * `get_symbols_overview`: Gets an overview of the top-level symbols defined in a given file.
+#  * `initial_instructions`: Gets the initial instructions for the current project.
+#     Should only be used in settings where the system prompt cannot be set,
+#     e.g. in clients you have no control over, like Claude Desktop.
+#  * `insert_after_symbol`: Inserts content after the end of the definition of a given symbol.
+#  * `insert_at_line`: Inserts content at a given line in a file.
+#  * `insert_before_symbol`: Inserts content before the beginning of the definition of a given symbol.
+#  * `list_dir`: Lists files and directories in the given directory (optionally with recursion).
+#  * `list_memories`: Lists memories in Serena's project-specific memory store.
+#  * `onboarding`: Performs onboarding (identifying the project structure and essential tasks, e.g. for testing or building).
+#  * `prepare_for_new_conversation`: Provides instructions for preparing for a new conversation (in order to continue with the necessary context).
+#  * `read_file`: Reads a file within the project directory.
+#  * `read_memory`: Reads the memory with the given name from Serena's project-specific memory store.
+#  * `remove_project`: Removes a project from the Serena configuration.
+#  * `replace_lines`: Replaces a range of lines within a file with new content.
+#  * `replace_symbol_body`: Replaces the full definition of a symbol.
+#  * `restart_language_server`: Restarts the language server, may be necessary when edits not through Serena happen.
+#  * `search_for_pattern`: Performs a search for a pattern in the project.
+#  * `summarize_changes`: Provides instructions for summarizing the changes made to the codebase.
+#  * `switch_modes`: Activates modes by providing a list of their names
+#  * `think_about_collected_information`: Thinking tool for pondering the completeness of collected information.
+#  * `think_about_task_adherence`: Thinking tool for determining whether the agent is still on track with the current task.
+#  * `think_about_whether_you_are_done`: Thinking tool for determining whether the task is truly completed.
+#  * `write_memory`: Writes a named memory (for future reference) to Serena's project-specific memory store.
+excluded_tools: []
+
+# initial prompt for the project. It will always be given to the LLM upon activating the project
+# (contrary to the memories, which are loaded on demand).
+initial_prompt: ""
+
+project_name: "yargi-mcp"
+included_optional_tools: []

Dockerfile

@@ -9,10 +9,11 @@ COPY pyproject.toml poetry.lock* requirements*.txt* ./
 
 # Fast, deterministic install with `uv`
 RUN pip install --no-cache-dir uv && \
+    uv pip install --system --no-cache-dir . && \
     uv pip install --system --no-cache-dir .[asgi,saas]
 
 # Cache buster - force rebuild
-ARG CACHE_BUST=202507221015
+ARG CACHE_BUST=202510061202
 RUN echo "Cache bust: $CACHE_BUST"
 
 # Copy application source

README.md

@@ -4,6 +4,28 @@
 
 Bu proje, çeşitli Türk hukuk kaynaklarına (Yargıtay, Danıştay, Emsal Kararlar, Uyuşmazlık Mahkemesi, Anayasa Mahkemesi - Norm Denetimi ile Bireysel Başvuru Kararları, Kamu İhale Kurulu Kararları, Rekabet Kurumu Kararları, Sayıştay Kararları, KVKK Kararları ve BDDK Kararları) erişimi kolaylaştıran bir [FastMCP](https://gofastmcp.com/) sunucusu oluşturur. Bu sayede, bu kaynaklardan veri arama ve belge getirme işlemleri, Model Context Protocol (MCP) destekleyen LLM (Büyük Dil Modeli) uygulamaları (örneğin Claude Desktop veya [5ire](https://5ire.app)) ve diğer istemciler tarafından araç (tool) olarak kullanılabilir hale gelir.
 
+---
+
+## 🚀 5 Dakikada Başla (Remote MCP)
+
+### ✅ Kurulum Gerektirmez! Hemen Kullan!
+
+🔗 **Remote MCP Adresi:** `https://yargimcp.fastmcp.app/mcp`
+
+### Claude Desktop ile Kullanım
+
+1. **Claude Desktop'ı açın**
+2. **Settings → Connectors → Add Custom Connector**
+3. **Bilgileri girin:**
+   - **Name:** `Yargı MCP`
+   - **URL:** `https://yargimcp.fastmcp.app/mcp`
+4. **Add** butonuna tıklayın
+5. **Hemen kullanmaya başlayın!** 🎉
+
+> 💡 **İpucu:** Remote MCP sayesinde Python, uv veya herhangi bir kurulum yapmadan doğrudan Claude Desktop üzerinden Türk hukuk kaynaklarına erişebilirsiniz!
+
+---
+
 ![örnek](./ornek.png)
 
 🎯 **Temel Özellikler**

analyze_kik_hash_generation.py

@@ -8,6 +8,7 @@ and trying to reverse engineer the hash generation logic.
 import asyncio
 import json
 import hashlib
+import hmac
 import base64
 from fastmcp import Client
 from mcp_server_main import app
@@ -151,7 +152,7 @@ async def test_hash_generation_comprehensive():
                 
                 # Test with first decision
                 sample_decision = decisions[0]
-                print("\n📋 Sample decision for hash analysis:")
+                print(f"\n📋 Sample decision for hash analysis:")
                 for key, value in sample_decision.items():
                     print(f"   {key}: {value}")
                 
@@ -161,20 +162,20 @@ async def test_hash_generation_comprehensive():
                 all_hashes = {}
                 
                 # Test different hash generation methods
-                print("\n🔨 Testing webpack-style hashing...")
+                print(f"\n🔨 Testing webpack-style hashing...")
                 webpack_hashes = test_webpack_style_hashing(sample_decision)
                 all_hashes.update(webpack_hashes)
                 
-                print("🔨 Testing Angular routing hashes...")  
+                print(f"🔨 Testing Angular routing hashes...")  
                 angular_hashes = test_angular_routing_hashes(sample_decision)
                 all_hashes.update(angular_hashes)
                 
-                print("🔨 Testing base64 encoding variants...")
+                print(f"🔨 Testing base64 encoding variants...")
                 b64_hashes = test_base64_encoding_variants(sample_decision)
                 all_hashes.update(b64_hashes)
                 
                 # Check for matches
-                print("\n🎯 Checking for hash matches...")
+                print(f"\n🎯 Checking for hash matches...")
                 matches_found = []
                 partial_matches = []
                 
@@ -190,13 +191,13 @@ async def test_hash_generation_comprehensive():
                         print(f"   🔍 Partial match (last 8): {hash_name} -> ...{hash_value[-16:]}")
                 
                 if not matches_found and not partial_matches:
-                    print("   ❌ No matches found")
+                    print(f"   ❌ No matches found")
-                    print("\n📝 Sample generated hashes (first 10):")
+                    print(f"\n📝 Sample generated hashes (first 10):")
                     for i, (hash_name, hash_value) in enumerate(list(all_hashes.items())[:10]):
                         print(f"   {hash_name}: {hash_value}")
                 
                 # Try combinations with other decisions
-                print("\n🔄 Testing hash combinations with multiple decisions...")
+                print(f"\n🔄 Testing hash combinations with multiple decisions...")
                 if len(decisions) > 1:
                     for i, decision in enumerate(decisions[1:3]):  # Test 2 more
                         print(f"\n   Testing decision {i+2}: {decision.get('kararNo')}")
@@ -208,7 +209,7 @@ async def test_hash_generation_comprehensive():
                                 matches_found.append((f"decision_{i+2}_{hash_name}", hash_value))
                 
                 # Try composite hashes (combining multiple fields)
-                print("\n🔗 Testing composite hash generation...")
+                print(f"\n🔗 Testing composite hash generation...")
                 composite_tests = [
                     f"{sample_decision.get('gundemMaddesiId')}_{sample_decision.get('kararNo')}",
                     f"{sample_decision.get('kararNo')}_{sample_decision.get('kararTarihi')}",
@@ -223,7 +224,7 @@ async def test_hash_generation_comprehensive():
                         print(f"   🎉 COMPOSITE MATCH FOUND: test_{i} -> {composite_str[:50]}...")
                         matches_found.append((f"composite_{i}", composite_hash))
                         
-                print("\n🎯 Hash analysis completed!")
+                print(f"\n🎯 Hash analysis completed!")
                 print(f"   Total matches found: {len(matches_found)}")
                 print(f"   Partial matches: {len(partial_matches)}")
                 

anayasa_mcp_module/bireysel_client.py

@@ -2,13 +2,13 @@
 # This client is for Bireysel Başvuru: https://kararlarbilgibankasi.anayasa.gov.tr
 
 import httpx
-from bs4 import BeautifulSoup
+from bs4 import BeautifulSoup, Tag
-from typing import List, Optional, Tuple
+from typing import Dict, Any, List, Optional, Tuple
 import logging
 import html
 import re
 import io
-from urllib.parse import urljoin
+from urllib.parse import urlencode, urljoin, quote
 from markitdown import MarkItDown
 import math # For math.ceil for pagination
 

anayasa_mcp_module/client.py

@@ -3,12 +3,12 @@
 
 import httpx
 from bs4 import BeautifulSoup
-from typing import List, Optional, Tuple
+from typing import Dict, Any, List, Optional, Tuple
 import logging
 import html
 import re
 import io
-from urllib.parse import urljoin
+from urllib.parse import urlencode, urljoin, quote
 from markitdown import MarkItDown
 import math # For math.ceil for pagination
 

anayasa_mcp_module/unified_client.py

@@ -2,6 +2,7 @@
 # Unified client for both Norm Denetimi and Bireysel Başvuru
 
 import logging
+from typing import Optional
 from urllib.parse import urlparse
 
 from .models import (

asgi_app.py

@@ -10,22 +10,38 @@ Usage:
 """
 
 import os
+import time
 import logging
 import json
+from datetime import datetime, timedelta
 from fastapi import FastAPI, Request, HTTPException, Query
 from fastapi.responses import JSONResponse, HTMLResponse, Response
 from fastapi.exception_handlers import http_exception_handler
 from starlette.middleware import Middleware
 from starlette.middleware.cors import CORSMiddleware
+from starlette.middleware.base import BaseHTTPMiddleware
 
 # Import the proper create_app function that includes all middleware
 from mcp_server_main import create_app
 
-# Import Stripe webhook router
+# Conditional auth-related imports (only if auth enabled)
-from stripe_webhook import router as stripe_router
+_auth_check = os.getenv("ENABLE_AUTH", "false").lower() == "true"
 
-# Import simplified MCP Auth HTTP adapter
+if _auth_check:
-from mcp_auth_http_simple import router as mcp_auth_router
+    # Import MCP Auth HTTP adapter (OAuth endpoints)
+    try:
+        from mcp_auth_http_simple import router as mcp_auth_router
+    except ImportError:
+        mcp_auth_router = None
+
+    # Import Stripe webhook router
+    try:
+        from stripe_webhook import router as stripe_router
+    except ImportError:
+        stripe_router = None
+else:
+    mcp_auth_router = None
+    stripe_router = None
 
 # OAuth configuration from environment variables
 CLERK_ISSUER = os.getenv("CLERK_ISSUER", "https://clerk.yargimcp.com")
@@ -59,15 +75,13 @@ if CLERK_SECRET_KEY and CLERK_ISSUER:
     # Production: Use Clerk JWKS endpoint for token validation
     bearer_auth = BearerAuthProvider(
         jwks_uri=f"{CLERK_ISSUER}/.well-known/jwks.json",
-        issuer=None,  # Disable issuer validation - Clerk uses different issuer format
+        issuer=None,
         algorithm="RS256",
-        audience=None,  # Disable audience validation - Clerk uses different audience format
+        audience=None,
-        required_scopes=[]  # Disable scope validation - Clerk JWT has ['read', 'search']
+        required_scopes=[]
     )
-    logger.info(f"Bearer auth configured with Clerk JWKS: {CLERK_ISSUER}/.well-known/jwks.json")
 else:
     # Development: Generate RSA key pair for testing
-    logger.warning("No Clerk credentials found - using development RSA key pair")
     dev_key_pair = RSAKeyPair.generate()
     bearer_auth = BearerAuthProvider(
         public_key=dev_key_pair.public_key,
@@ -76,30 +90,12 @@ else:
         required_scopes=["yargi.read"]
     )
 
-    # Generate a test token for development
-    dev_token = dev_key_pair.create_token(
-        subject="dev-user",
-        issuer="https://dev.yargimcp.com",
-        audience="dev-mcp-server",
-        scopes=["yargi.read", "yargi.search"],
-        expires_in_seconds=3600 * 24  # 24 hours for development
-    )
-    logger.info(f"Development Bearer token: {dev_token}")
-
 # Create MCP app with Bearer authentication
 mcp_server = create_app(auth=bearer_auth if auth_enabled else None)
 
 # Create MCP Starlette sub-application with root path - mount will add /mcp prefix
 mcp_app = mcp_server.http_app(path="/")
-logger.info(f"MCP Starlette app created - type: {type(mcp_app)}, has routes: {hasattr(mcp_app, 'routes')}")
 
-# Debug FastMCP routes
-if hasattr(mcp_app, 'routes'):
-    logger.info(f"MCP app route count: {len(mcp_app.routes)}")
-    for i, route in enumerate(mcp_app.routes):
-        logger.info(f"Route {i}: {route.path if hasattr(route, 'path') else 'unknown'} - {type(route)}")
-else:
-    logger.warning("MCP app has no routes attribute")
 
 # Configure JSON encoder for proper Turkish character support
 class UTF8JSONResponse(JSONResponse):
@@ -138,11 +134,12 @@ app = FastAPI(
     redirect_slashes=False  # Disable to prevent 307 redirects on /mcp endpoint
 )
 
-# Add Stripe webhook router to FastAPI
+# Add auth-related routers to FastAPI (only if available)
-app.include_router(stripe_router, prefix="/api/stripe")
+if stripe_router:
+    app.include_router(stripe_router, prefix="/api/stripe")
 
-# Add MCP Auth HTTP adapter to FastAPI (handles OAuth endpoints)
+if mcp_auth_router:
-app.include_router(mcp_auth_router)
+    app.include_router(mcp_auth_router)
 
 # Custom 401 exception handler for MCP spec compliance
 @app.exception_handler(401)
@@ -254,7 +251,6 @@ async def clerk_cors_proxy(request: Request, path: str):
             )
             
     except Exception as e:
-        logger.error(f"Clerk proxy error: {e}")
         return JSONResponse(
             {"error": "proxy_error", "message": str(e)},
             status_code=500,
@@ -448,7 +444,6 @@ async def status():
 # Simplified OAuth session validation for callback endpoints only
 async def validate_clerk_session_for_oauth(request: Request, clerk_token: str = None) -> str:
     """Validate Clerk session for OAuth callback endpoints only (not for MCP endpoints)"""
-    logger.info(f"OAuth callback session validation - token provided: {bool(clerk_token)}")
     
     try:
         # Use Clerk SDK if available
@@ -458,58 +453,43 @@ async def validate_clerk_session_for_oauth(request: Request, clerk_token: str =
         
         # Try JWT token first (from URL parameter)
         if clerk_token:
-            logger.info("Validating Clerk JWT token for OAuth callback")
             try:
-                # Trust OAuth flow redirect - FastMCP handles full JWT validation for MCP endpoints
-                logger.info("OAuth JWT token accepted for callback")
                 return "oauth_user_from_token"
             except Exception as e:
-                logger.error(f"OAuth JWT token validation failed: {str(e)}")
+                pass
-                # Fall through to cookie validation
 
         # Fallback to cookie validation
-        logger.info("Attempting cookie-based session validation for OAuth")
         clerk_session = request.cookies.get("__session")
         if not clerk_session:
-            logger.error("No Clerk session cookie found")
             raise HTTPException(status_code=401, detail="No Clerk session found")
 
         # Validate session with Clerk
         session = clerk.sessions.verify_session(clerk_session)
-        logger.info(f"OAuth cookie session validation successful - user_id: {session.user_id}")
         return session.user_id
         
     except ImportError:
-        # Fallback for development without Clerk SDK
-        logger.warning("Clerk SDK not available - using development fallback for OAuth")
         return "dev_user_123"
     except Exception as e:
-        logger.error(f"OAuth session validation failed: {str(e)}")
         raise HTTPException(status_code=401, detail=f"OAuth session validation failed: {str(e)}")
 
 # MCP OAuth Callback Endpoint
 @app.get("/auth/mcp-callback")
 async def mcp_oauth_callback(request: Request, clerk_token: str = Query(None)):
     """Handle OAuth callback for MCP token generation"""
-    logger.info(f"MCP OAuth callback - clerk_token provided: {bool(clerk_token)}")
     
     try:
         # Validate Clerk session with JWT token support
         user_id = await validate_clerk_session_for_oauth(request, clerk_token)
-        logger.info(f"User authenticated successfully - user_id: {user_id}")
-        
-        # Use the Clerk JWT token directly (no need to generate custom token)
-        logger.info("User authenticated successfully via Clerk")
         
         # Return success response
-        return HTMLResponse("""
+        return HTMLResponse(f"""
         <html>
             <head>
                 <title>MCP Connection Successful</title>
                 <style>
-                    body { font-family: Arial, sans-serif; text-align: center; padding: 50px; }
+                    body {{ font-family: Arial, sans-serif; text-align: center; padding: 50px; }}
-                    .success { color: #28a745; }
+                    .success {{ color: #28a745; }}
-                    .token { background: #f8f9fa; padding: 15px; border-radius: 5px; margin: 20px 0; word-break: break-all; }
+                    .token {{ background: #f8f9fa; padding: 15px; border-radius: 5px; margin: 20px 0; word-break: break-all; }}
                 </style>
             </head>
             <body>
@@ -522,20 +502,19 @@ async def mcp_oauth_callback(request: Request, clerk_token: str = Query(None)):
                 <p>You can now close this window and return to your MCP client.</p>
                 <script>
                     // Try to close the popup if opened as such
-                    if (window.opener) {
+                    if (window.opener) {{
-                        window.opener.postMessage({
+                        window.opener.postMessage({{
                             type: 'MCP_AUTH_SUCCESS',
                             token: 'use_clerk_jwt_token'
-                        }, '*');
+                        }}, '*');
                         setTimeout(() => window.close(), 3000);
-                    }
+                    }}
                 </script>
             </body>
         </html>
         """)
         
     except HTTPException as e:
-        logger.error(f"MCP OAuth callback failed: {e.detail}")
         return HTMLResponse(f"""
         <html>
             <head>
@@ -561,7 +540,6 @@ async def mcp_oauth_callback(request: Request, clerk_token: str = Query(None)):
         </html>
         """, status_code=e.status_code)
     except Exception as e:
-        logger.error(f"Unexpected error in MCP OAuth callback: {str(e)}")
         return HTMLResponse(f"""
         <html>
             <head>
@@ -607,7 +585,5 @@ app.mount("/mcp/", mcp_app)
 # Set the lifespan context after mounting
 app.router.lifespan_context = mcp_app.lifespan
 
-logger.info("MCP app mounted successfully at /mcp/")
-
 # Export for uvicorn
 __all__ = ["app"]

bddk_mcp_module/client.py

@@ -1,12 +1,13 @@
 # bddk_mcp_module/client.py
 
 import httpx
-from typing import Optional
+from typing import List, Optional, Dict, Any
 import logging
 import os
 import re
 import io
 import math
+from urllib.parse import urlparse
 from markitdown import MarkItDown
 
 from .models import (

bddk_mcp_module/models.py

@@ -1,7 +1,7 @@
 # bddk_mcp_module/models.py
 
 from pydantic import BaseModel, Field
-from typing import List
+from typing import List, Optional
 
 class BddkSearchRequest(BaseModel):
     """

bedesten_mcp_module/models.py

@@ -1,7 +1,8 @@
 # bedesten_mcp_module/models.py
 
 from pydantic import BaseModel, Field
-from typing import List, Optional, Dict, Any, Literal
+from typing import List, Optional, Dict, Any, Literal, Union
+from datetime import datetime
 
 # Import compressed BirimAdiEnum for chamber filtering
 from .enums import BirimAdiEnum

danistay_mcp_module/client.py

@@ -1,9 +1,11 @@
 # danistay_mcp_module/client.py
 
 import httpx
-from typing import Dict, List, Optional
+from bs4 import BeautifulSoup 
+from typing import Dict, Any, List, Optional
 import logging
 import html
+import re
 import io
 from markitdown import MarkItDown
 

emsal_mcp_module/client.py

@@ -2,9 +2,10 @@
 
 import httpx
 # from bs4 import BeautifulSoup # Uncomment if needed for advanced HTML pre-processing
-from typing import Dict, Optional
+from typing import Dict, Any, List, Optional
 import logging
 import html
+import re
 import io
 from markitdown import MarkItDown
 

example_fastapi_app.py

@@ -7,9 +7,11 @@ import os
 from typing import List, Dict, Any, Optional
 from datetime import datetime
 
-from fastapi import FastAPI, HTTPException, Query
+from fastapi import FastAPI, HTTPException, Query, Depends, Body
 from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import JSONResponse
 from pydantic import BaseModel, Field
+import json
 
 # Import the main MCP app
 from mcp_server_main import app as mcp_server

kik_mcp_module/client.py

@@ -10,12 +10,13 @@ from playwright.async_api import (
 )
 from bs4 import BeautifulSoup
 import logging
-from typing import List, Optional
+from typing import Dict, Any, List, Optional
 import urllib.parse 
 import base64 # Base64 için
 import re
 import html as html_parser 
 from markitdown import MarkItDown 
+import os
 import math 
 import io
 import random
@@ -906,7 +907,7 @@ class KikApiClient:
                     event_target_for_submit = self.FIELD_LOCATORS['search_button_id']
                     # Use human-like clicking for search button
                     search_button_selector = f"a[id='{event_target_for_submit}']"
-                    logger.info("Performing human-like search button click...")
+                    logger.info(f"Performing human-like search button click...")
                     
                     try:
                         # Hide datepicker first to prevent interference
@@ -1180,7 +1181,7 @@ class KikApiClient:
             try: 
                 if await current_main_page.locator(self.MODAL_CLOSE_BUTTON_SELECTOR).is_visible(timeout=2000): 
                     await current_main_page.locator(self.MODAL_CLOSE_BUTTON_SELECTOR).click()
-                    await current_main_page.wait_for_selector("div#detayPopUp:not(.in)", timeout=5000) 
+                    await current_main_page.wait_for_selector(f"div#detayPopUp:not(.in)", timeout=5000) 
             except: pass
 
             return KikDocumentMarkdown(

kik_mcp_module/client_v2.py

@@ -1,9 +1,13 @@
 # kik_mcp_module/client_v2.py
 
 import httpx
+import requests
 import logging
 import uuid
+import base64
 import ssl
+from typing import Optional
+from datetime import datetime
 
 from .models_v2 import (
     KikV2DecisionType, KikV2SearchPayload, KikV2SearchPayloadDk, KikV2SearchPayloadMk,
@@ -37,7 +41,8 @@ class KikV2ApiClient:
         ssl_context.check_hostname = False
         ssl_context.verify_mode = ssl.CERT_NONE
         
-        # Enable legacy server connect option for older SSL implementations
+        # Enable legacy server connect option for older SSL implementations (Python 3.12+)
+        if hasattr(ssl, 'OP_LEGACY_SERVER_CONNECT'):
             ssl_context.options |= ssl.OP_LEGACY_SERVER_CONNECT
         
         # Set broader cipher suite support including legacy ciphers

kik_mcp_module/models.py

@@ -1,5 +1,5 @@
 # kik_mcp_module/models.py
-from pydantic import BaseModel, Field, computed_field, ConfigDict
+from pydantic import BaseModel, Field, HttpUrl, computed_field, ConfigDict
 from typing import List, Optional
 from enum import Enum
 import base64 # Base64 encoding/decoding için

kik_mcp_module/models_v2.py

@@ -1,6 +1,7 @@
 # kik_mcp_module/models_v2.py
 from pydantic import BaseModel, Field, ConfigDict
-from typing import List
+from typing import List, Optional
+from datetime import datetime
 from enum import Enum
 
 # New KIK v2 API Models

kvkk_mcp_module/client.py

@@ -2,13 +2,13 @@
 
 import httpx
 from bs4 import BeautifulSoup
-from typing import Optional, Dict, Any
+from typing import List, Optional, Dict, Any
 import logging
 import os
 import re
 import io
 import math
-from urllib.parse import urlparse
+from urllib.parse import urljoin, urlparse, parse_qs
 from markitdown import MarkItDown
 from pydantic import HttpUrl
 

kvkk_mcp_module/models.py

@@ -1,7 +1,7 @@
 # kvkk_mcp_module/models.py
 
 from pydantic import BaseModel, Field, HttpUrl
-from typing import List, Optional
+from typing import List, Optional, Any
 
 class KvkkSearchRequest(BaseModel):
     """Model for KVKK (Personal Data Protection Authority) search request via Brave API."""

mcp_auth/clerk_config.py

@@ -36,7 +36,7 @@ def create_clerk_oauth_config() -> OAuthConfig:
         scopes=["mcp:tools:read", "mcp:tools:write", "openid", "profile", "email"]
     )
     
-    logger.info("Created Clerk OAuth config with adapter endpoints")
+    logger.info(f"Created Clerk OAuth config with adapter endpoints")
     logger.info(f"Clerk domain: {clerk_domain}")
     logger.debug(f"Authorization endpoint: {config.authorization_endpoint}")
     logger.debug(f"Token endpoint: {config.token_endpoint}")

mcp_auth/oauth.py

@@ -9,7 +9,7 @@ import time
 import logging
 from dataclasses import dataclass
 from datetime import datetime, timedelta
-from typing import Any
+from typing import Any, Optional
 from urllib.parse import urlencode
 
 import httpx

mcp_auth_http_adapter.py

@@ -18,7 +18,7 @@ from fastapi.responses import RedirectResponse, JSONResponse
 try:
     from clerk_backend_api import Clerk
     CLERK_AVAILABLE = True
-except ImportError:
+except ImportError as e:
     CLERK_AVAILABLE = False
     Clerk = None
 

mcp_auth_http_simple.py

@@ -6,7 +6,7 @@ Uses Redis for authorization code storage to support multi-machine deployment
 import os
 import logging
 from typing import Optional
-from urllib.parse import urlencode
+from urllib.parse import urlencode, quote
 
 from fastapi import APIRouter, Request, Query, HTTPException
 from fastapi.responses import RedirectResponse, JSONResponse
@@ -39,6 +39,7 @@ def get_redis_session_store():
     if redis_store is None:
         try:
             import concurrent.futures
+            import functools
             
             # Use thread pool with timeout to prevent hanging
             with concurrent.futures.ThreadPoolExecutor(max_workers=1) as executor:
@@ -205,6 +206,7 @@ async def oauth_callback(
             auth_code = f"clerk_auth_{os.urandom(16).hex()}"
             
             # Prepare code data
+            import time
             code_data = {
                 "user_id": user_id,
                 "session_id": session_id,
@@ -223,7 +225,7 @@ async def oauth_callback(
                 if success:
                     logger.info(f"Stored authorization code {auth_code[:10]}... in Redis with real JWT token")
                 else:
-                    logger.error("Failed to store authorization code in Redis, falling back to in-memory")
+                    logger.error(f"Failed to store authorization code in Redis, falling back to in-memory")
                     # Fall back to in-memory storage
                     if not hasattr(oauth_callback, '_code_storage'):
                         oauth_callback._code_storage = {}
@@ -234,7 +236,7 @@ async def oauth_callback(
                 if not hasattr(oauth_callback, '_code_storage'):
                     oauth_callback._code_storage = {}
                 oauth_callback._code_storage[auth_code] = code_data
-                logger.info("Stored authorization code in memory (fallback)")
+                logger.info(f"Stored authorization code in memory (fallback)")
             
             # Redirect back to client with authorization code
             redirect_params = {

mcp_server_main.py

@@ -7,10 +7,18 @@ import httpx
 import json
 import time
 from collections import defaultdict
-from pydantic import HttpUrl, Field 
+from pydantic import BaseModel, HttpUrl, Field 
-from typing import Dict, List, Literal, Any
+from typing import Optional, Dict, List, Literal, Any, Union
-import tiktoken
+import urllib.parse
 from fastmcp.server.middleware import Middleware, MiddlewareContext
+
+# Optional tiktoken import for token counting
+try:
+    import tiktoken
+    TIKTOKEN_AVAILABLE = True
+except ImportError:
+    TIKTOKEN_AVAILABLE = False
+    tiktoken = None
 from fastmcp.server.dependencies import get_access_token, AccessToken
 from fastmcp import Context
 
@@ -20,22 +28,11 @@ class ToolError(Exception):
     pass
 
 # --- Logging Configuration Start ---
-LOG_DIRECTORY = os.path.join(os.path.dirname(os.path.abspath(__file__)), "logs")
-if not os.path.exists(LOG_DIRECTORY):
-    os.makedirs(LOG_DIRECTORY)
-LOG_FILE_PATH = os.path.join(LOG_DIRECTORY, "mcp_server.log")
-
 root_logger = logging.getLogger()
-root_logger.setLevel(logging.DEBUG) 
+root_logger.setLevel(logging.INFO)
-
-log_formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(threadName)s - %(message)s')
-
-file_handler = logging.FileHandler(LOG_FILE_PATH, mode='a', encoding='utf-8')
-file_handler.setFormatter(log_formatter)
-file_handler.setLevel(logging.DEBUG)
-root_logger.addHandler(file_handler)
 
 console_handler = logging.StreamHandler()
+log_formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
 console_handler.setFormatter(log_formatter)
 console_handler.setLevel(logging.INFO)
 root_logger.addHandler(console_handler)
@@ -53,18 +50,13 @@ class TokenCountingMiddleware(Middleware):
         Args:
             model: Tiktoken model name (cl100k_base for GPT-4/Claude compatibility)
         """
+        if not TIKTOKEN_AVAILABLE:
+            raise ImportError("tiktoken is required for token counting. Install with: pip install tiktoken")
+
         self.encoder = tiktoken.get_encoding(model)
         self.model = model
         self.token_stats = defaultdict(lambda: {"input": 0, "output": 0, "calls": 0})
         self.logger = logging.getLogger("token_counter")
-        
-        # Create separate log file for token metrics
-        token_log_path = os.path.join(LOG_DIRECTORY, "token_metrics.log")
-        token_handler = logging.FileHandler(token_log_path, mode='a', encoding='utf-8')
-        token_formatter = logging.Formatter('%(asctime)s - %(message)s')
-        token_handler.setFormatter(token_formatter)
-        token_handler.setLevel(logging.INFO)
-        self.logger.addHandler(token_handler)
         self.logger.setLevel(logging.INFO)
     
     def count_tokens(self, text: str) -> int:
@@ -158,7 +150,7 @@ class TokenCountingMiddleware(Middleware):
             
             return result
             
-        except Exception:
+        except Exception as e:
             duration_ms = (time.perf_counter() - start_time) * 1000
             self.log_token_usage("tool_call_error", input_tokens, 0, 
                                tool_name, duration_ms)
@@ -188,7 +180,7 @@ class TokenCountingMiddleware(Middleware):
             
             return result
             
-        except Exception:
+        except Exception as e:
             duration_ms = (time.perf_counter() - start_time) * 1000
             self.log_token_usage("resource_read_error", 0, 0, 
                                resource_uri, duration_ms)
@@ -218,7 +210,7 @@ class TokenCountingMiddleware(Middleware):
             
             return result
             
-        except Exception:
+        except Exception as e:
             duration_ms = (time.perf_counter() - start_time) * 1000
             self.log_token_usage("prompt_get_error", 0, 0, 
                                prompt_name, duration_ms)
@@ -246,26 +238,34 @@ def create_app(auth=None):
     else:
         logger.info("MCP server created with standard capabilities...")
     
+    # Add token counting middleware only if tiktoken is available
+    if TIKTOKEN_AVAILABLE:
+        try:
             token_counter = TokenCountingMiddleware()
             app.add_middleware(token_counter)
             logger.info("Token counting middleware added to MCP server")
+        except Exception as e:
+            logger.warning(f"Failed to add token counting middleware: {e}")
     
     return app
 
 # --- Module Imports ---
 from yargitay_mcp_module.client import YargitayOfficialApiClient
+from yargitay_mcp_module.models import (
+    YargitayDetailedSearchRequest, YargitayDocumentMarkdown, CompactYargitaySearchResult,
+    YargitayBirimEnum, CleanYargitayDecisionEntry
+)
 from bedesten_mcp_module.client import BedestenApiClient
 from bedesten_mcp_module.models import (
     BedestenSearchRequest, BedestenSearchData,
     BedestenDocumentMarkdown, BedestenCourtTypeEnum
 )
 from bedesten_mcp_module.enums import BirimAdiEnum
-
-# Semantic Search Module Imports
-from semantic_search.embedder import EmbeddingGemma
-from semantic_search.vector_store import VectorStore
-from semantic_search.processor import DocumentProcessor
 from danistay_mcp_module.client import DanistayApiClient
+from danistay_mcp_module.models import (
+    DanistayKeywordSearchRequest, DanistayDetailedSearchRequest,
+    DanistayDocumentMarkdown, CompactDanistaySearchResult
+)
 from emsal_mcp_module.client import EmsalApiClient
 from emsal_mcp_module.models import (
     EmsalSearchRequest, EmsalDocumentMarkdown, CompactEmsalSearchResult
@@ -279,12 +279,24 @@ from anayasa_mcp_module.client import AnayasaMahkemesiApiClient
 from anayasa_mcp_module.bireysel_client import AnayasaBireyselBasvuruApiClient
 from anayasa_mcp_module.unified_client import AnayasaUnifiedClient
 from anayasa_mcp_module.models import (
+    AnayasaNormDenetimiSearchRequest,
+    AnayasaSearchResult,
+    AnayasaDocumentMarkdown,
+    AnayasaBireyselReportSearchRequest,
+    AnayasaBireyselReportSearchResult,
+    AnayasaBireyselBasvuruDocumentMarkdown,
     AnayasaUnifiedSearchRequest,
+    AnayasaUnifiedSearchResult,
+    AnayasaUnifiedDocumentMarkdown,
     # Removed enum imports - now using Literal strings in models
 )
 # KIK v2 Module Imports (New API)
 from kik_mcp_module.client_v2 import KikV2ApiClient
 from kik_mcp_module.models_v2 import KikV2DecisionType
+from kik_mcp_module.models_v2 import (
+    KikV2SearchResult,
+    KikV2DocumentMarkdown
+)
 
 from rekabet_mcp_module.client import RekabetKurumuApiClient
 from rekabet_mcp_module.models import (
@@ -296,9 +308,14 @@ from rekabet_mcp_module.models import (
 
 from sayistay_mcp_module.client import SayistayApiClient
 from sayistay_mcp_module.models import (
+    GenelKurulSearchRequest, GenelKurulSearchResponse,
+    TemyizKuruluSearchRequest, TemyizKuruluSearchResponse,
+    DaireSearchRequest, DaireSearchResponse,
+    SayistayDocumentMarkdown,
     SayistayUnifiedSearchRequest, SayistayUnifiedSearchResult,
     SayistayUnifiedDocumentMarkdown
 )
+from sayistay_mcp_module.enums import DaireEnum, KamuIdaresiTuruEnum, WebKararKonusuEnum
 from sayistay_mcp_module.unified_client import SayistayUnifiedClient
 
 # KVKK Module Imports
@@ -312,11 +329,14 @@ from kvkk_mcp_module.models import (
 # BDDK Module Imports
 from bddk_mcp_module.client import BddkApiClient
 from bddk_mcp_module.models import (
-    BddkSearchRequest
+    BddkSearchRequest,
+    BddkSearchResult,
+    BddkDocumentMarkdown
 )
 
 
 # Create a placeholder app that will be properly initialized after tools are defined
+from fastmcp import FastMCP
 
 # MCP app for Turkish legal databases with explicit capabilities
 app = FastMCP(
@@ -342,6 +362,9 @@ sayistay_unified_client_instance = SayistayUnifiedClient()
 kvkk_client_instance = KvkkApiClient()
 bddk_client_instance = BddkApiClient()
 
+# Health check client (singleton for reuse)
+_health_check_client: Optional[httpx.AsyncClient] = None
+
 
 KARAR_TURU_ADI_TO_GUID_ENUM_MAP = {
     "": RekabetKararTuruGuidEnum.TUMU,  # Keep for backward compatibility
@@ -599,7 +622,7 @@ async def search_emsal_detailed_decisions(
     sort_direction: str = Field("desc", description="Sorting direction ('asc' or 'desc')."),
     page_number: int = Field(1, ge=1, description="Page number (accepts int)."),
     # page_size: int = Field(10, ge=1, le=10, description="Results per page.")
-) -> CompactEmsalSearchResult:
+) -> Dict[str, Any]:
     """Search Emsal precedent decisions with detailed criteria."""
     
     page_size = 10  # Default value
@@ -623,7 +646,7 @@ async def search_emsal_detailed_decisions(
         page_size=page_size
     )
     
-    logger.info("Tool 'search_emsal_detailed_decisions' called.")
+    logger.info(f"Tool 'search_emsal_detailed_decisions' called.")
     try:
         api_response = await emsal_client_instance.search_detailed_decisions(search_query)
         if api_response.data:
@@ -632,11 +655,11 @@ async def search_emsal_detailed_decisions(
                 total_records=api_response.data.recordsTotal if api_response.data.recordsTotal is not None else 0,
                 requested_page=search_query.page_number,
                 page_size=search_query.page_size
-            )
+            ).model_dump()
         logger.warning("API response for Emsal search did not contain expected data structure.")
-        return CompactEmsalSearchResult(decisions=[], total_records=0, requested_page=search_query.page_number, page_size=search_query.page_size)
+        return CompactEmsalSearchResult(decisions=[], total_records=0, requested_page=search_query.page_number, page_size=search_query.page_size).model_dump()
-    except Exception:
+    except Exception as e:
-        logger.exception("Error in tool 'search_emsal_detailed_decisions'.")
+        logger.exception(f"Error in tool 'search_emsal_detailed_decisions'.")
         raise
 
 @app.tool(
@@ -646,14 +669,15 @@ async def search_emsal_detailed_decisions(
         "idempotentHint": True
     }
 )
-async def get_emsal_document_markdown(id: str) -> EmsalDocumentMarkdown:
+async def get_emsal_document_markdown(id: str) -> Dict[str, Any]:
     """Get document as Markdown."""
     logger.info(f"Tool 'get_emsal_document_markdown' called for ID: {id}")
     if not id or not id.strip(): raise ValueError("Document ID required for Emsal.")
     try:
-        return await emsal_client_instance.get_decision_document_as_markdown(id)
+        result = await emsal_client_instance.get_decision_document_as_markdown(id)
-    except Exception:
+        return result.model_dump()
-        logger.exception("Error in tool 'get_emsal_document_markdown'.")
+    except Exception as e:
+        logger.exception(f"Error in tool 'get_emsal_document_markdown'.")
         raise
 
 # --- MCP Tools for Uyusmazlik ---
@@ -684,7 +708,7 @@ async def search_uyusmazlik_decisions(
     hepsi: str = Field("", description="Search for texts containing all specified words."),
     herhangi_birisi: str = Field("", description="Search for texts containing any of the specified words."),
     not_hepsi: str = Field("", description="Exclude texts containing these specified words.")
-) -> UyusmazlikSearchResponse:
+) -> Dict[str, Any]:
     """Search Court of Jurisdictional Disputes decisions."""
     
     # Convert string literals to enums
@@ -721,11 +745,12 @@ async def search_uyusmazlik_decisions(
         not_hepsi=not_hepsi
     )
     
-    logger.info("Tool 'search_uyusmazlik_decisions' called.")
+    logger.info(f"Tool 'search_uyusmazlik_decisions' called.")
     try:
-        return await uyusmazlik_client_instance.search_decisions(search_params)
+        result = await uyusmazlik_client_instance.search_decisions(search_params)
-    except Exception:
+        return result.model_dump()
-        logger.exception("Error in tool 'search_uyusmazlik_decisions'.")
+    except Exception as e:
+        logger.exception(f"Error in tool 'search_uyusmazlik_decisions'.")
         raise
 
 @app.tool(
@@ -737,15 +762,16 @@ async def search_uyusmazlik_decisions(
 )
 async def get_uyusmazlik_document_markdown_from_url(
     document_url: str = Field(..., description="Full URL to the Uyuşmazlık Mahkemesi decision document from search results")
-) -> UyusmazlikDocumentMarkdown:
+) -> Dict[str, Any]:
     """Get Uyuşmazlık Mahkemesi decision as Markdown."""
     logger.info(f"Tool 'get_uyusmazlik_document_markdown_from_url' called for URL: {str(document_url)}")
     if not document_url:
         raise ValueError("Document URL (document_url) is required for Uyuşmazlık document retrieval.")
     try:
-        return await uyusmazlik_client_instance.get_decision_document_as_markdown(str(document_url))
+        result = await uyusmazlik_client_instance.get_decision_document_as_markdown(str(document_url))
-    except Exception:
+        return result.model_dump()
-        logger.exception("Error in tool 'get_uyusmazlik_document_markdown_from_url'.")
+    except Exception as e:
+        logger.exception(f"Error in tool 'get_uyusmazlik_document_markdown_from_url'.")
         raise
 
 # --- DEACTIVATED: MCP Tools for Anayasa Mahkemesi (Individual Tools) ---
@@ -836,8 +862,8 @@ async def search_anayasa_unified(
         result = await anayasa_unified_client_instance.search_unified(request)
         return json.dumps(result.model_dump(), ensure_ascii=False, indent=2)
         
-    except Exception:
+    except Exception as e:
-        logger.exception("Error in tool 'search_anayasa_unified'.")
+        logger.exception(f"Error in tool 'search_anayasa_unified'.")
         raise
 
 @app.tool(
@@ -858,8 +884,8 @@ async def get_anayasa_document_unified(
         result = await anayasa_unified_client_instance.get_document_unified(document_url, page_number)
         return json.dumps(result.model_dump(mode='json'), ensure_ascii=False, indent=2)
         
-    except Exception:
+    except Exception as e:
-        logger.exception("Error in tool 'get_anayasa_document_unified'.")
+        logger.exception(f"Error in tool 'get_anayasa_document_unified'.")
         raise
 
 # --- MCP Tools for KIK v2 (Kamu İhale Kurulu - New API) ---
@@ -1007,7 +1033,7 @@ async def search_rekabet_kurumu_decisions(
     KararSayisi: str = Field("", description="Decision number (Karar Sayısı)."),
     KararTarihi: str = Field("", description="Decision date (Karar Tarihi), e.g., DD.MM.YYYY."),
     page: int = Field(1, ge=1, description="Page number to fetch for the results list.")
-) -> RekabetSearchResult:
+) -> Dict[str, Any]:
     """Search Competition Authority decisions."""
     
     karar_turu_guid_enum = KARAR_TURU_ADI_TO_GUID_ENUM_MAP.get(KararTuru)
@@ -1032,10 +1058,11 @@ async def search_rekabet_kurumu_decisions(
     logger.info(f"Tool 'search_rekabet_kurumu_decisions' called. Query: {search_query.model_dump_json(exclude_none=True, indent=2)}")
     try:
        
-        return await rekabet_client_instance.search_decisions(search_query)
+        result = await rekabet_client_instance.search_decisions(search_query)
-    except Exception:
+        return result.model_dump()
+    except Exception as e:
         logger.exception("Error in tool 'search_rekabet_kurumu_decisions'.")
-        return RekabetSearchResult(decisions=[], retrieved_page_number=page, total_records_found=0, total_pages=0)
+        return RekabetSearchResult(decisions=[], retrieved_page_number=page, total_records_found=0, total_pages=0).model_dump()
 
 @app.tool(
     description="Get Competition Authority decision text in paginated Markdown format",
@@ -1047,16 +1074,16 @@ async def search_rekabet_kurumu_decisions(
 async def get_rekabet_kurumu_document(
     karar_id: str = Field(..., description="GUID (kararId) of the Rekabet Kurumu decision. This ID is obtained from search results."),
     page_number: int = Field(1, ge=1, description="Requested page number for the Markdown content converted from PDF (1-indexed, accepts int). Default is 1.")
-) -> RekabetDocument:
+) -> Dict[str, Any]:
     """Get Competition Authority decision as paginated Markdown."""
     logger.info(f"Tool 'get_rekabet_kurumu_document' called. Karar ID: {karar_id}, Markdown Page: {page_number}")
     
     current_page_to_fetch = page_number if page_number >= 1 else 1
     
     try:
-      
+        result = await rekabet_client_instance.get_decision_document(karar_id, page_number=current_page_to_fetch)
-        return await rekabet_client_instance.get_decision_document(karar_id, page_number=current_page_to_fetch)
+        return result.model_dump()
-    except Exception:
+    except Exception as e:
         logger.exception(f"Error in tool 'get_rekabet_kurumu_document'. Karar ID: {karar_id}")
         raise 
 
@@ -1167,7 +1194,7 @@ For best results, use exact phrases with quotes for legal terms."""),
             "page_size": pageSize,
             "searched_courts": court_types
         }
-    except Exception:
+    except Exception as e:
         logger.exception("Error in tool 'search_bedesten_unified'")
         raise
 
@@ -1189,7 +1216,7 @@ async def get_bedesten_document_markdown(
     
     try:
         return await bedesten_client_instance.get_document_as_markdown(documentId)
-    except Exception:
+    except Exception as e:
         logger.exception("Error in tool 'get_kyb_bedesten_document_markdown'")
         raise
 
@@ -1351,7 +1378,7 @@ async def search_sayistay_unified(
     yargilama_dairesi: Literal["ALL", "1", "2", "3", "4", "5", "6", "7", "8"] = Field("ALL", description="Chamber selection (daire only)"),
     hesap_yili: str = Field("", description="Account year (daire only)"),
     web_karar_metni: str = Field("", description="Decision text search (daire only)")
-) -> SayistayUnifiedSearchResult:
+) -> Dict[str, Any]:
     """Search Sayıştay decisions across all three decision types with unified interface."""
     logger.info(f"Tool 'search_sayistay_unified' called with decision_type={decision_type}")
 
@@ -1377,8 +1404,9 @@ async def search_sayistay_unified(
             hesap_yili=hesap_yili,
             web_karar_metni=web_karar_metni
         )
-        return await sayistay_unified_client_instance.search_unified(search_request)
+        result = await sayistay_unified_client_instance.search_unified(search_request)
-    except Exception:
+        return result.model_dump()
+    except Exception as e:
         logger.exception("Error in tool 'search_sayistay_unified'")
         raise
 
@@ -1393,7 +1421,7 @@ async def search_sayistay_unified(
 async def get_sayistay_document_unified(
     decision_id: str = Field(..., description="Decision ID from search_sayistay_unified results"),
     decision_type: Literal["genel_kurul", "temyiz_kurulu", "daire"] = Field(..., description="Decision type: genel_kurul, temyiz_kurulu, or daire")
-) -> SayistayUnifiedDocumentMarkdown:
+) -> Dict[str, Any]:
     """Get Sayıştay decision document as Markdown for any decision type."""
     logger.info(f"Tool 'get_sayistay_document_unified' called for ID: {decision_id}, type: {decision_type}")
 
@@ -1401,8 +1429,9 @@ async def get_sayistay_document_unified(
         raise ValueError("Decision ID must be a non-empty string.")
 
     try:
-        return await sayistay_unified_client_instance.get_document_unified(decision_id, decision_type)
+        result = await sayistay_unified_client_instance.get_document_unified(decision_id, decision_type)
-    except Exception:
+        return result.model_dump()
+    except Exception as e:
         logger.exception("Error in tool 'get_sayistay_document_unified'")
         raise
 
@@ -1439,6 +1468,11 @@ def perform_cleanup():
             if client_instance and hasattr(client_instance, 'close_client_session') and callable(client_instance.close_client_session):
                 logger.info(f"Scheduling close for client session: {client_instance.__class__.__name__}")
                 tasks.append(client_instance.close_client_session())
+        # Close health check client if it was created
+        global _health_check_client
+        if _health_check_client is not None:
+            logger.info("Closing health check HTTP client")
+            tasks.append(_health_check_client.aclose())
         if tasks:
             results = await asyncio.gather(*tasks, return_exceptions=True)
             for i, result in enumerate(results):
@@ -1460,6 +1494,19 @@ def perform_cleanup():
 
 atexit.register(perform_cleanup)
 
+
+def get_or_create_health_check_client() -> httpx.AsyncClient:
+    """Get or create a reusable HTTP client for health checks."""
+    global _health_check_client
+    if _health_check_client is None:
+        _health_check_client = httpx.AsyncClient(
+            timeout=10.0,
+            verify=False,
+            follow_redirects=True
+        )
+    return _health_check_client
+
+
 # --- Health Check Tools ---
 @app.tool(
     description="Check if Turkish government legal database servers are operational",
@@ -1626,7 +1673,7 @@ async def search_kvkk_decisions(
     keywords: str = Field(..., description="Turkish keywords. Supports +required -excluded \"exact phrase\" operators"),
     page: int = Field(1, ge=1, le=50, description="Page number for results (1-50)."),
     # pageSize: int = Field(10, ge=1, le=20, description="Number of results per page (1-20).")
-) -> KvkkSearchResult:
+) -> Dict[str, Any]:
     """Search function for legal decisions."""
     logger.info(f"KVKK search tool called with keywords: {keywords}")
 
@@ -1641,7 +1688,7 @@ async def search_kvkk_decisions(
     try:
         result = await kvkk_client_instance.search_decisions(search_request)
         logger.info(f"KVKK search completed. Found {len(result.decisions)} decisions on page {page}")
-        return result
+        return result.model_dump()
     except Exception as e:
         logger.exception(f"Error in KVKK search: {e}")
         # Return empty result on error
@@ -1651,7 +1698,7 @@ async def search_kvkk_decisions(
             page=page,
             pageSize=pageSize,
             query=keywords
-        )
+        ).model_dump()
 
 @app.tool(
     description="Get KVKK decision document in Markdown format with metadata extraction",
@@ -1664,7 +1711,7 @@ async def search_kvkk_decisions(
 async def get_kvkk_document_markdown(
     decision_url: str = Field(..., description="KVKK decision URL from search results"),
     page_number: int = Field(1, ge=1, description="Page number for paginated Markdown content (1-indexed, accepts int). Default is 1 (first 5,000 characters).")
-) -> KvkkDocumentMarkdown:
+) -> Dict[str, Any]:
     """Get KVKK decision as paginated Markdown."""
     logger.info(f"KVKK document retrieval tool called for URL: {decision_url}")
 
@@ -1680,7 +1727,7 @@ async def get_kvkk_document_markdown(
             total_pages=0,
             is_paginated=False,
             error_message="Decision URL is required and cannot be empty."
-        )
+        ).model_dump()
     
     try:
         # Validate URL format
@@ -1696,11 +1743,11 @@ async def get_kvkk_document_markdown(
                 total_pages=0,
                 is_paginated=False,
                 error_message="Invalid KVKK decision URL format. URL must start with https://www.kvkk.gov.tr/"
-            )
+            ).model_dump()
 
         result = await kvkk_client_instance.get_decision_document(decision_url, page_number or 1)
         logger.info(f"KVKK document retrieved successfully. Page {result.current_page}/{result.total_pages}, Content length: {len(result.markdown_chunk) if result.markdown_chunk else 0}")
-        return result
+        return result.model_dump()
         
     except Exception as e:
         logger.exception(f"Error retrieving KVKK document: {e}")
@@ -1715,7 +1762,7 @@ async def get_kvkk_document_markdown(
             total_pages=0,
             is_paginated=False,
             error_message=f"Error retrieving KVKK document: {str(e)}"
-        )
+        ).model_dump()
 
 # --- MCP Tools for BDDK (Banking Regulation Authority) ---
 @app.tool(
@@ -1815,234 +1862,6 @@ async def get_bddk_document_markdown(
             "error": str(e)
         }
 
-# --- Semantic Search Tool ---
-
-@app.tool(
-    description="Semantic search for Turkish legal decisions using EmbeddingGemma for intelligent ranking",
-    annotations={
-        "readOnlyHint": True,
-        "openWorldHint": True,
-        "idempotentHint": True
-    }
-)
-async def search_bedesten_semantic(
-    query: str = Field(..., description="Search query in Turkish for semantic matching"),
-    initial_keyword: str = Field(..., description="Initial keyword for Bedesten API search (broad term)"),
-    court_types: List[BedestenCourtTypeEnum] = Field(
-        default=["YARGITAYKARARI", "DANISTAYKARAR", "YERELHUKUK", "ISTINAFHUKUK", "KYB"],
-        description="Court types to search: YARGITAYKARARI, DANISTAYKARAR, YERELHUKUK, ISTINAFHUKUK, KYB (default: all)"
-    ),
-    top_k: int = Field(10, ge=1, le=50, description="Number of top results to return (1-50)")
-) -> Dict[str, Any]:
-    """
-    Perform semantic search on Turkish legal decisions using EmbeddingGemma.
-    
-    This tool:
-    1. Searches Bedesten API with initial keyword (retrieves 100 results)
-    2. Fetches full document content for each result
-    3. Generates embeddings using Google's EmbeddingGemma model
-    4. Performs semantic similarity search with the query
-    5. Returns re-ranked results based on semantic relevance
-    
-    Benefits over keyword search:
-    - Better understanding of context and meaning
-    - Finds semantically similar documents even with different wording
-    - More accurate ranking based on relevance
-    - Supports multilingual queries (100+ languages)
-    """
-    logger.info(f"Semantic search tool called with query: {query}, keyword: {initial_keyword}")
-    
-    try:
-        # Initialize components
-        embedder = EmbeddingGemma()
-        vector_store = VectorStore(dimension=256)  # Always use 256 for optimal speed/quality balance
-        processor = DocumentProcessor(chunk_size=1500, chunk_overlap=300)
-        
-        # Step 1: Initial keyword search to get document IDs
-        logger.info(f"Step 1: Searching Bedesten API with keyword: {initial_keyword}")
-        
-        all_decisions = []
-        
-        # Search each court type
-        for court_type in court_types:
-            try:
-                # Calculate page size per court type to get 100 total
-                per_court_limit = max(20, 100 // len(court_types))
-                
-                search_results = await bedesten_client_instance.search_documents(
-                    BedestenSearchRequest(
-                        data=BedestenSearchData(
-                            phrase=initial_keyword,
-                            itemTypeList=[court_type],
-                            pageSize=per_court_limit,  # Distribute 100 across court types
-                            pageNumber=1
-                        )
-                    )
-                )
-                
-                if search_results.data and search_results.data.emsalKararList:
-                    all_decisions.extend(search_results.data.emsalKararList)
-                    logger.info(f"Found {len(search_results.data.emsalKararList)} results from {court_type}")
-                
-            except Exception as e:
-                logger.warning(f"Error searching {court_type}: {e}")
-        
-        if not all_decisions:
-            logger.warning("No documents found from initial search")
-            return {
-                "status": "no_results",
-                "message": "No documents found matching the initial keyword",
-                "results": []
-            }
-        
-        logger.info(f"Total documents found: {len(all_decisions)}")
-        
-        # Step 2: Fetch document content and process
-        logger.info("Step 2: Fetching and processing document content...")
-        
-        documents_data = []
-        failed_fetches = 0
-        
-        # Process up to 100 documents total
-        decisions_to_process = all_decisions[:100]
-        
-        for i, decision in enumerate(decisions_to_process):
-            try:
-                # Fetch document content
-                doc = await bedesten_client_instance.get_document_as_markdown(decision.documentId)
-                
-                if doc.markdown_content:
-                    # Process document into chunks
-                    metadata = {
-                        "document_id": decision.documentId,
-                        "birim_adi": decision.birimAdi,
-                        "esas_no": decision.esasNo,
-                        "karar_no": decision.kararNo,
-                        "karar_tarihi": decision.kararTarihiStr,
-                        "court_type": decision.itemType.name if decision.itemType else None
-                    }
-                    
-                    chunks = processor.process_document(
-                        document_id=decision.documentId,
-                        text=doc.markdown_content,
-                        metadata=metadata
-                    )
-                    
-                    # For now, use the full document as one chunk (can be optimized later)
-                    if chunks:
-                        full_text = " ".join([chunk.text for chunk in chunks])
-                        documents_data.append({
-                            "id": decision.documentId,
-                            "text": full_text[:3000],  # Limit text for embedding
-                            "metadata": metadata
-                        })
-                
-                # Log progress every 10 documents
-                if (i + 1) % 10 == 0:
-                    logger.info(f"Processed {i + 1}/{len(decisions_to_process)} documents")
-                    
-            except Exception as e:
-                logger.warning(f"Failed to fetch document {decision.documentId}: {e}")
-                failed_fetches += 1
-        
-        if not documents_data:
-            logger.warning("No documents could be processed")
-            return {
-                "status": "processing_error",
-                "message": "Could not process any documents",
-                "results": []
-            }
-        
-        logger.info(f"Successfully processed {len(documents_data)} documents, {failed_fetches} failed")
-        
-        # Step 3: Generate embeddings
-        logger.info("Step 3: Generating embeddings...")
-        
-        # Generate query embedding
-        query_embedding = embedder.encode_query(query, task="search result")
-        
-        # Generate document embeddings
-        doc_texts = [doc["text"] for doc in documents_data]
-        doc_titles = [doc["metadata"].get("birim_adi", "none") for doc in documents_data]
-        doc_embeddings = embedder.encode_documents(doc_texts, titles=doc_titles)
-        
-        # Always reduce to 256 dimensions for optimal speed/quality balance
-        query_embedding = embedder.reduce_dimensions(query_embedding, 256)
-        doc_embeddings = embedder.reduce_dimensions(doc_embeddings, 256)
-        
-        # Step 4: Add to vector store and search
-        logger.info("Step 4: Performing semantic search...")
-        
-        # Add documents to vector store
-        doc_ids = [doc["id"] for doc in documents_data]
-        doc_metadatas = [doc["metadata"] for doc in documents_data]
-        
-        vector_store.add_documents(
-            ids=doc_ids,
-            texts=doc_texts,
-            embeddings=doc_embeddings,
-            metadata=doc_metadatas
-        )
-        
-        # Perform semantic search
-        search_results = vector_store.search(
-            query_embedding=query_embedding,
-            top_k=top_k,
-            threshold=0.3  # Minimum similarity threshold
-        )
-        
-        # Step 5: Format results
-        logger.info(f"Step 5: Formatting {len(search_results)} results")
-        
-        formatted_results = []
-        for doc, score in search_results:
-            # Build title from metadata
-            title_parts = []
-            if doc.metadata.get("birim_adi"):
-                title_parts.append(doc.metadata["birim_adi"])
-            if doc.metadata.get("esas_no"):
-                title_parts.append(f"Esas: {doc.metadata['esas_no']}")
-            if doc.metadata.get("karar_no"):
-                title_parts.append(f"Karar: {doc.metadata['karar_no']}")
-            if doc.metadata.get("karar_tarihi"):
-                title_parts.append(f"Tarih: {doc.metadata['karar_tarihi']}")
-            
-            title = " - ".join(title_parts) if title_parts else f"Document {doc.id}"
-            
-            formatted_results.append({
-                "document_id": doc.id,
-                "title": title,
-                "similarity_score": float(score),
-                "preview": doc.text[:500] + "..." if len(doc.text) > 500 else doc.text,
-                "metadata": doc.metadata,
-                "source_url": f"https://mevzuat.adalet.gov.tr/ictihat/{doc.id}"
-            })
-        
-        # Get vector store stats
-        stats = vector_store.get_stats()
-        
-        return {
-            "status": "success",
-            "query": query,
-            "initial_keyword": initial_keyword,
-            "total_documents_processed": len(documents_data),
-            "embedding_dimension": 256,  # Fixed at 256 for optimal performance
-            "results": formatted_results,
-            "stats": {
-                "documents_in_store": stats["num_documents"],
-                "memory_usage_mb": round(stats["memory_usage_mb"], 2),
-                "failed_fetches": failed_fetches
-            }
-        }
-        
-    except Exception as e:
-        logger.exception(f"Error in semantic search: {e}")
-        return {
-            "status": "error",
-            "message": str(e),
-            "results": []
-        }
-
 # --- ChatGPT Deep Research Compatible Tools ---
 
 def get_preview_text(markdown_content: str, skip_chars: int = 100, preview_chars: int = 200) -> str:
@@ -2090,6 +1909,7 @@ def get_preview_text(markdown_content: str, skip_chars: int = 100, preview_chars
     
     return preview.strip()
 
+
 @app.tool(
     description="DO NOT USE unless you are ChatGPT Deep Research. Search Turkish courts (Turkish keywords only). Supports: +term (must have), -term (exclude), \"exact phrase\", term1 OR term2",
     annotations={
@@ -2100,7 +1920,7 @@ def get_preview_text(markdown_content: str, skip_chars: int = 100, preview_chars
 )
 async def search(
     query: str = Field(..., description="Turkish search query")
-) -> Dict[str, List[Dict[str, str]]]:
+) -> Dict[str, Any]:
     """
     Bedesten API search tool for ChatGPT Deep Research compatibility.
     
@@ -2214,13 +2034,33 @@ async def search(
         """
         
         logger.info(f"ChatGPT Deep Research search completed. Found {len(results)} results via Bedesten API.")
-        return {"results": results}
+        return {
+            "results": [
+                {
+                    "id": item["id"],
+                    "title": item["title"],
+                    "text": item["text"],
+                    "url": item["url"]
+                }
+                for item in results
+            ]
+        }
         
-    except Exception:
+    except Exception as e:
         logger.exception("Error in ChatGPT Deep Research search tool")
         # Return partial results if any were found
         if results:
-            return {"results": results}
+            return {
+                "results": [
+                    {
+                        "id": item["id"],
+                        "title": item["title"],
+                        "text": item["text"],
+                        "url": item["url"]
+                    }
+                    for item in results
+                ]
+            }
         raise
 
 @app.tool(
@@ -2345,7 +2185,7 @@ async def fetch(
             doc = await bedesten_client_instance.get_document_as_markdown(doc_id)
         """
         
-    except Exception:
+    except Exception as e:
         logger.exception(f"Error fetching ChatGPT Deep Research document {id}")
         raise
 
@@ -2387,7 +2227,7 @@ def main():
     app = create_app()
     
     logger.info(f"Starting {app.name} server via main() function...")
-    logger.info(f"Logs will be written to: {LOG_FILE_PATH}")
+    # logger.info(f"Logs will be written to: {LOG_FILE_PATH}")  # File logging disabled
     
     # Ensure Playwright browsers are installed
     ensure_playwright_browsers()
@@ -2396,7 +2236,7 @@ def main():
         app.run()
     except KeyboardInterrupt: 
         logger.info("Server shut down by user (KeyboardInterrupt).")
-    except Exception: 
+    except Exception as e: 
         logger.exception("Server failed to start or crashed.")
     finally:
         logger.info(f"{app.name} server has shut down.")

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "yargi-mcp"
-version = "0.1.7"
+version = "0.1.9"
 description = "MCP Server For Turkish Legal Databases"
 readme = "README.md"
 requires-python = ">=3.11"
@@ -29,13 +29,6 @@ dependencies = [
     "fastmcp>=2.10.5",
     "pypdf>=5.5.0",
     "fastapi>=0.115.14",
-    "PyJWT>=2.8.0",
-    "tiktoken>=0.5.0",
-    "sentence-transformers>=3.0.0",
-    "torch>=2.0.0",
-    "numpy>=1.24.0",
-    "scikit-learn>=1.3.0",
-    "transformers>=4.57.0.dev0",
 ]
 
 [project.optional-dependencies]
@@ -55,6 +48,8 @@ saas = [
     "clerk-backend-api>=3.0.0",
     "stripe>=9.1.0",
     "upstash-redis>=1.1.0",
+    "tiktoken>=0.5.0",
+    "PyJWT>=2.8.0",
 ]
 
 [project.scripts]

redis_session_store.py

@@ -11,8 +11,8 @@ import os
 import json
 import time
 import logging
-from typing import Optional, Dict, Any
+from typing import Optional, Dict, Any, Union
-from datetime import datetime
+from datetime import datetime, timedelta
 
 logger = logging.getLogger(__name__)
 

rekabet_mcp_module/client.py

@@ -4,9 +4,10 @@ import httpx
 from bs4 import BeautifulSoup
 from typing import List, Optional, Tuple, Dict, Any
 import logging
+import html
 import re
 import io # For io.BytesIO
-from urllib.parse import urljoin, parse_qs, urlparse
+from urllib.parse import urlencode, urljoin, quote, parse_qs, urlparse
 from markitdown import MarkItDown
 import math
 
@@ -17,7 +18,8 @@ from .models import (
     RekabetKurumuSearchRequest,
     RekabetDecisionSummary,
     RekabetSearchResult,
-    RekabetDocument
+    RekabetDocument,
+    RekabetKararTuruGuidEnum
 )
 from pydantic import HttpUrl # Ensure HttpUrl is imported from pydantic
 

rekabet_mcp_module/models.py

@@ -1,7 +1,7 @@
 # rekabet_mcp_module/models.py
 
 from pydantic import BaseModel, Field, HttpUrl
-from typing import List, Optional
+from typing import List, Optional, Any
 from enum import Enum
 
 # Enum for decision type GUIDs (used by the client and expected by the website)

run_asgi.py

@@ -93,7 +93,7 @@ def main():
         config["workers"] = args.workers
     
     # Print startup information
-    print("Starting Yargı MCP server...")
+    print(f"Starting Yargı MCP server...")
     print(f"Host: {args.host}")
     print(f"Port: {args.port}")
     print(f"Transport: {args.transport}")

saidsurucu-yargi-mcp-f5fa007/anayasa_mcp_module/bireysel_client.py

@@ -2,13 +2,13 @@
 # This client is for Bireysel Başvuru: https://kararlarbilgibankasi.anayasa.gov.tr
 
 import httpx
-from bs4 import BeautifulSoup
+from bs4 import BeautifulSoup, Tag
-from typing import List, Optional, Tuple
+from typing import Dict, Any, List, Optional, Tuple
 import logging
 import html
 import re
 import io
-from urllib.parse import urljoin
+from urllib.parse import urlencode, urljoin, quote
 from markitdown import MarkItDown
 import math # For math.ceil for pagination
 

saidsurucu-yargi-mcp-f5fa007/anayasa_mcp_module/client.py

@@ -3,12 +3,12 @@
 
 import httpx
 from bs4 import BeautifulSoup
-from typing import List, Optional, Tuple
+from typing import Dict, Any, List, Optional, Tuple
 import logging
 import html
 import re
 import io
-from urllib.parse import urljoin
+from urllib.parse import urlencode, urljoin, quote
 from markitdown import MarkItDown
 import math # For math.ceil for pagination
 

saidsurucu-yargi-mcp-f5fa007/anayasa_mcp_module/unified_client.py

@@ -2,6 +2,7 @@
 # Unified client for both Norm Denetimi and Bireysel Başvuru
 
 import logging
+from typing import Optional
 from urllib.parse import urlparse
 
 from .models import (

saidsurucu-yargi-mcp-f5fa007/asgi_app.py

@@ -10,12 +10,16 @@ Usage:
 """
 
 import os
+import time
 import logging
+from datetime import datetime, timedelta
 from fastapi import FastAPI, Request, HTTPException, Query
 from fastapi.responses import JSONResponse, HTMLResponse
 from fastapi.exception_handlers import http_exception_handler
 from starlette.middleware import Middleware
 from starlette.middleware.cors import CORSMiddleware
+from starlette.responses import Response
+from starlette.requests import Request as StarletteRequest
 
 # Import the MCP app creator function
 from mcp_server_main import create_app
@@ -99,6 +103,7 @@ mcp_app = mcp_server.http_app(path="/")
 
 # Configure JSON encoder for proper Turkish character support
 import json
+from fastapi.responses import JSONResponse
 
 class UTF8JSONResponse(JSONResponse):
     def __init__(self, content=None, status_code=200, headers=None, **kwargs):
@@ -456,14 +461,14 @@ async def mcp_oauth_callback(request: Request, clerk_token: str = Query(None)):
         logger.info("User authenticated successfully via Clerk")
         
         # Return success response
-        return HTMLResponse("""
+        return HTMLResponse(f"""
         <html>
             <head>
                 <title>MCP Connection Successful</title>
                 <style>
-                    body { font-family: Arial, sans-serif; text-align: center; padding: 50px; }
+                    body {{ font-family: Arial, sans-serif; text-align: center; padding: 50px; }}
-                    .success { color: #28a745; }
+                    .success {{ color: #28a745; }}
-                    .token { background: #f8f9fa; padding: 15px; border-radius: 5px; margin: 20px 0; word-break: break-all; }
+                    .token {{ background: #f8f9fa; padding: 15px; border-radius: 5px; margin: 20px 0; word-break: break-all; }}
                 </style>
             </head>
             <body>
@@ -476,13 +481,13 @@ async def mcp_oauth_callback(request: Request, clerk_token: str = Query(None)):
                 <p>You can now close this window and return to your MCP client.</p>
                 <script>
                     // Try to close the popup if opened as such
-                    if (window.opener) {
+                    if (window.opener) {{
-                        window.opener.postMessage({
+                        window.opener.postMessage({{
                             type: 'MCP_AUTH_SUCCESS',
                             token: 'use_clerk_jwt_token'
-                        }, '*');
+                        }}, '*');
                         setTimeout(() => window.close(), 3000);
-                    }
+                    }}
                 </script>
             </body>
         </html>

saidsurucu-yargi-mcp-f5fa007/bddk_mcp_module/client.py

@@ -1,12 +1,13 @@
 # bddk_mcp_module/client.py
 
 import httpx
-from typing import Optional
+from typing import List, Optional, Dict, Any
 import logging
 import os
 import re
 import io
 import math
+from urllib.parse import urlparse
 from markitdown import MarkItDown
 
 from .models import (

saidsurucu-yargi-mcp-f5fa007/bddk_mcp_module/models.py

@@ -1,7 +1,7 @@
 # bddk_mcp_module/models.py
 
 from pydantic import BaseModel, Field
-from typing import List
+from typing import List, Optional
 
 class BddkSearchRequest(BaseModel):
     """

saidsurucu-yargi-mcp-f5fa007/bedesten_mcp_module/models.py

@@ -1,7 +1,8 @@
 # bedesten_mcp_module/models.py
 
 from pydantic import BaseModel, Field
-from typing import List, Optional, Dict, Any, Literal
+from typing import List, Optional, Dict, Any, Literal, Union
+from datetime import datetime
 
 # Import compressed BirimAdiEnum for chamber filtering
 from .enums import BirimAdiEnum

saidsurucu-yargi-mcp-f5fa007/danistay_mcp_module/client.py

@@ -1,9 +1,11 @@
 # danistay_mcp_module/client.py
 
 import httpx
-from typing import Dict, List, Optional
+from bs4 import BeautifulSoup 
+from typing import Dict, Any, List, Optional
 import logging
 import html
+import re
 import io
 from markitdown import MarkItDown
 

saidsurucu-yargi-mcp-f5fa007/emsal_mcp_module/client.py

@@ -2,9 +2,10 @@
 
 import httpx
 # from bs4 import BeautifulSoup # Uncomment if needed for advanced HTML pre-processing
-from typing import Dict, Optional
+from typing import Dict, Any, List, Optional
 import logging
 import html
+import re
 import io
 from markitdown import MarkItDown
 

saidsurucu-yargi-mcp-f5fa007/example_fastapi_app.py

@@ -7,9 +7,11 @@ import os
 from typing import List, Dict, Any, Optional
 from datetime import datetime
 
-from fastapi import FastAPI, HTTPException, Query
+from fastapi import FastAPI, HTTPException, Query, Depends, Body
 from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import JSONResponse
 from pydantic import BaseModel, Field
+import json
 
 # Import the main MCP app
 from mcp_server_main import app as mcp_server

saidsurucu-yargi-mcp-f5fa007/kik_mcp_module/client.py

@@ -10,12 +10,13 @@ from playwright.async_api import (
 )
 from bs4 import BeautifulSoup
 import logging
-from typing import List, Optional
+from typing import Dict, Any, List, Optional
 import urllib.parse 
 import base64 # Base64 için
 import re
 import html as html_parser 
 from markitdown import MarkItDown 
+import os
 import math 
 import io
 import random
@@ -906,7 +907,7 @@ class KikApiClient:
                     event_target_for_submit = self.FIELD_LOCATORS['search_button_id']
                     # Use human-like clicking for search button
                     search_button_selector = f"a[id='{event_target_for_submit}']"
-                    logger.info("Performing human-like search button click...")
+                    logger.info(f"Performing human-like search button click...")
                     
                     try:
                         # Hide datepicker first to prevent interference
@@ -1180,7 +1181,7 @@ class KikApiClient:
             try: 
                 if await current_main_page.locator(self.MODAL_CLOSE_BUTTON_SELECTOR).is_visible(timeout=2000): 
                     await current_main_page.locator(self.MODAL_CLOSE_BUTTON_SELECTOR).click()
-                    await current_main_page.wait_for_selector("div#detayPopUp:not(.in)", timeout=5000) 
+                    await current_main_page.wait_for_selector(f"div#detayPopUp:not(.in)", timeout=5000) 
             except: pass
 
             return KikDocumentMarkdown(

saidsurucu-yargi-mcp-f5fa007/kik_mcp_module/models.py

@@ -1,5 +1,5 @@
 # kik_mcp_module/models.py
-from pydantic import BaseModel, Field, computed_field, ConfigDict
+from pydantic import BaseModel, Field, HttpUrl, computed_field, ConfigDict
 from typing import List, Optional
 from enum import Enum
 import base64 # Base64 encoding/decoding için

saidsurucu-yargi-mcp-f5fa007/kvkk_mcp_module/client.py

@@ -2,13 +2,13 @@
 
 import httpx
 from bs4 import BeautifulSoup
-from typing import Optional, Dict, Any
+from typing import List, Optional, Dict, Any
 import logging
 import os
 import re
 import io
 import math
-from urllib.parse import urlparse
+from urllib.parse import urljoin, urlparse, parse_qs
 from markitdown import MarkItDown
 from pydantic import HttpUrl
 

saidsurucu-yargi-mcp-f5fa007/kvkk_mcp_module/models.py

@@ -1,7 +1,7 @@
 # kvkk_mcp_module/models.py
 
 from pydantic import BaseModel, Field, HttpUrl
-from typing import List, Optional
+from typing import List, Optional, Any
 
 class KvkkSearchRequest(BaseModel):
     """Model for KVKK (Personal Data Protection Authority) search request via Brave API."""

saidsurucu-yargi-mcp-f5fa007/mcp_auth/clerk_config.py

@@ -36,7 +36,7 @@ def create_clerk_oauth_config() -> OAuthConfig:
         scopes=["mcp:tools:read", "mcp:tools:write", "openid", "profile", "email"]
     )
     
-    logger.info("Created Clerk OAuth config with adapter endpoints")
+    logger.info(f"Created Clerk OAuth config with adapter endpoints")
     logger.info(f"Clerk domain: {clerk_domain}")
     logger.debug(f"Authorization endpoint: {config.authorization_endpoint}")
     logger.debug(f"Token endpoint: {config.token_endpoint}")

saidsurucu-yargi-mcp-f5fa007/mcp_auth/oauth.py

@@ -9,7 +9,7 @@ import time
 import logging
 from dataclasses import dataclass
 from datetime import datetime, timedelta
-from typing import Any
+from typing import Any, Optional
 from urllib.parse import urlencode
 
 import httpx

saidsurucu-yargi-mcp-f5fa007/mcp_auth_http_adapter.py

@@ -18,7 +18,7 @@ from fastapi.responses import RedirectResponse, JSONResponse
 try:
     from clerk_backend_api import Clerk
     CLERK_AVAILABLE = True
-except ImportError:
+except ImportError as e:
     CLERK_AVAILABLE = False
     Clerk = None
 

saidsurucu-yargi-mcp-f5fa007/mcp_auth_http_simple.py

@@ -6,7 +6,7 @@ Uses Redis for authorization code storage to support multi-machine deployment
 import os
 import logging
 from typing import Optional
-from urllib.parse import urlencode
+from urllib.parse import urlencode, quote
 
 from fastapi import APIRouter, Request, Query, HTTPException
 from fastapi.responses import RedirectResponse, JSONResponse
@@ -39,6 +39,7 @@ def get_redis_session_store():
     if redis_store is None:
         try:
             import concurrent.futures
+            import functools
             
             # Use thread pool with timeout to prevent hanging
             with concurrent.futures.ThreadPoolExecutor(max_workers=1) as executor:
@@ -205,6 +206,7 @@ async def oauth_callback(
             auth_code = f"clerk_auth_{os.urandom(16).hex()}"
             
             # Prepare code data
+            import time
             code_data = {
                 "user_id": user_id,
                 "session_id": session_id,
@@ -223,7 +225,7 @@ async def oauth_callback(
                 if success:
                     logger.info(f"Stored authorization code {auth_code[:10]}... in Redis with real JWT token")
                 else:
-                    logger.error("Failed to store authorization code in Redis, falling back to in-memory")
+                    logger.error(f"Failed to store authorization code in Redis, falling back to in-memory")
                     # Fall back to in-memory storage
                     if not hasattr(oauth_callback, '_code_storage'):
                         oauth_callback._code_storage = {}
@@ -234,7 +236,7 @@ async def oauth_callback(
                 if not hasattr(oauth_callback, '_code_storage'):
                     oauth_callback._code_storage = {}
                 oauth_callback._code_storage[auth_code] = code_data
-                logger.info("Stored authorization code in memory (fallback)")
+                logger.info(f"Stored authorization code in memory (fallback)")
             
             # Redirect back to client with authorization code
             redirect_params = {

saidsurucu-yargi-mcp-f5fa007/mcp_server_main.py

@@ -8,7 +8,8 @@ import json
 import time
 from collections import defaultdict
 from pydantic import HttpUrl, Field 
-from typing import Dict, List, Literal, Any
+from typing import Optional, Dict, List, Literal, Any, Union
+import urllib.parse
 import tiktoken
 from fastmcp.server.middleware import Middleware, MiddlewareContext
 from fastmcp.server.dependencies import get_access_token, AccessToken
@@ -158,7 +159,7 @@ class TokenCountingMiddleware(Middleware):
             
             return result
             
-        except Exception:
+        except Exception as e:
             duration_ms = (time.perf_counter() - start_time) * 1000
             self.log_token_usage("tool_call_error", input_tokens, 0, 
                                tool_name, duration_ms)
@@ -188,7 +189,7 @@ class TokenCountingMiddleware(Middleware):
             
             return result
             
-        except Exception:
+        except Exception as e:
             duration_ms = (time.perf_counter() - start_time) * 1000
             self.log_token_usage("resource_read_error", 0, 0, 
                                resource_uri, duration_ms)
@@ -218,7 +219,7 @@ class TokenCountingMiddleware(Middleware):
             
             return result
             
-        except Exception:
+        except Exception as e:
             duration_ms = (time.perf_counter() - start_time) * 1000
             self.log_token_usage("prompt_get_error", 0, 0, 
                                prompt_name, duration_ms)
@@ -259,6 +260,10 @@ def create_app(auth=None):
 
 # --- Module Imports ---
 from yargitay_mcp_module.client import YargitayOfficialApiClient
+from yargitay_mcp_module.models import (
+    YargitayDetailedSearchRequest, YargitayDocumentMarkdown, CompactYargitaySearchResult,
+    YargitayBirimEnum, CleanYargitayDecisionEntry
+)
 from bedesten_mcp_module.client import BedestenApiClient
 from bedesten_mcp_module.models import (
     BedestenSearchRequest, BedestenSearchData,
@@ -266,6 +271,10 @@ from bedesten_mcp_module.models import (
 )
 from bedesten_mcp_module.enums import BirimAdiEnum
 from danistay_mcp_module.client import DanistayApiClient
+from danistay_mcp_module.models import (
+    DanistayKeywordSearchRequest, DanistayDetailedSearchRequest,
+    DanistayDocumentMarkdown, CompactDanistaySearchResult
+)
 from emsal_mcp_module.client import EmsalApiClient
 from emsal_mcp_module.models import (
     EmsalSearchRequest, EmsalDocumentMarkdown, CompactEmsalSearchResult
@@ -279,7 +288,15 @@ from anayasa_mcp_module.client import AnayasaMahkemesiApiClient
 from anayasa_mcp_module.bireysel_client import AnayasaBireyselBasvuruApiClient
 from anayasa_mcp_module.unified_client import AnayasaUnifiedClient
 from anayasa_mcp_module.models import (
+    AnayasaNormDenetimiSearchRequest,
+    AnayasaSearchResult,
+    AnayasaDocumentMarkdown,
+    AnayasaBireyselReportSearchRequest,
+    AnayasaBireyselReportSearchResult,
+    AnayasaBireyselBasvuruDocumentMarkdown,
     AnayasaUnifiedSearchRequest,
+    AnayasaUnifiedSearchResult,
+    AnayasaUnifiedDocumentMarkdown,
     # Removed enum imports - now using Literal strings in models
 )
 # KIK Module Imports
@@ -301,9 +318,14 @@ from rekabet_mcp_module.models import (
 
 from sayistay_mcp_module.client import SayistayApiClient
 from sayistay_mcp_module.models import (
+    GenelKurulSearchRequest, GenelKurulSearchResponse,
+    TemyizKuruluSearchRequest, TemyizKuruluSearchResponse,
+    DaireSearchRequest, DaireSearchResponse,
+    SayistayDocumentMarkdown,
     SayistayUnifiedSearchRequest, SayistayUnifiedSearchResult,
     SayistayUnifiedDocumentMarkdown
 )
+from sayistay_mcp_module.enums import DaireEnum, KamuIdaresiTuruEnum, WebKararKonusuEnum
 from sayistay_mcp_module.unified_client import SayistayUnifiedClient
 
 # KVKK Module Imports
@@ -317,11 +339,14 @@ from kvkk_mcp_module.models import (
 # BDDK Module Imports
 from bddk_mcp_module.client import BddkApiClient
 from bddk_mcp_module.models import (
-    BddkSearchRequest
+    BddkSearchRequest,
+    BddkSearchResult,
+    BddkDocumentMarkdown
 )
 
 
 # Create a placeholder app that will be properly initialized after tools are defined
+from fastmcp import FastMCP
 
 # Placeholder app for decorators - will be replaced in create_app() after all tools are defined
 app = FastMCP("Yargı MCP Server Placeholder")
@@ -1347,7 +1372,7 @@ async def search_emsal_detailed_decisions(
         page_size=page_size
     )
     
-    logger.info("Tool 'search_emsal_detailed_decisions' called.")
+    logger.info(f"Tool 'search_emsal_detailed_decisions' called.")
     try:
         api_response = await emsal_client_instance.search_detailed_decisions(search_query)
         if api_response.data:
@@ -1359,8 +1384,8 @@ async def search_emsal_detailed_decisions(
             )
         logger.warning("API response for Emsal search did not contain expected data structure.")
         return CompactEmsalSearchResult(decisions=[], total_records=0, requested_page=search_query.page_number, page_size=search_query.page_size)
-    except Exception:
+    except Exception as e:
-        logger.exception("Error in tool 'search_emsal_detailed_decisions'.")
+        logger.exception(f"Error in tool 'search_emsal_detailed_decisions'.")
         raise
 
 @app.tool(
@@ -1376,8 +1401,8 @@ async def get_emsal_document_markdown(id: str) -> EmsalDocumentMarkdown:
     if not id or not id.strip(): raise ValueError("Document ID required for Emsal.")
     try:
         return await emsal_client_instance.get_decision_document_as_markdown(id)
-    except Exception:
+    except Exception as e:
-        logger.exception("Error in tool 'get_emsal_document_markdown'.")
+        logger.exception(f"Error in tool 'get_emsal_document_markdown'.")
         raise
 
 # --- MCP Tools for Uyusmazlik ---
@@ -1445,11 +1470,11 @@ async def search_uyusmazlik_decisions(
         not_hepsi=not_hepsi
     )
     
-    logger.info("Tool 'search_uyusmazlik_decisions' called.")
+    logger.info(f"Tool 'search_uyusmazlik_decisions' called.")
     try:
         return await uyusmazlik_client_instance.search_decisions(search_params)
-    except Exception:
+    except Exception as e:
-        logger.exception("Error in tool 'search_uyusmazlik_decisions'.")
+        logger.exception(f"Error in tool 'search_uyusmazlik_decisions'.")
         raise
 
 @app.tool(
@@ -1468,8 +1493,8 @@ async def get_uyusmazlik_document_markdown_from_url(
         raise ValueError("Document URL (document_url) is required for Uyuşmazlık document retrieval.")
     try:
         return await uyusmazlik_client_instance.get_decision_document_as_markdown(str(document_url))
-    except Exception:
+    except Exception as e:
-        logger.exception("Error in tool 'get_uyusmazlik_document_markdown_from_url'.")
+        logger.exception(f"Error in tool 'get_uyusmazlik_document_markdown_from_url'.")
         raise
 
 # --- DEACTIVATED: MCP Tools for Anayasa Mahkemesi (Individual Tools) ---
@@ -1560,8 +1585,8 @@ async def search_anayasa_unified(
         result = await anayasa_unified_client_instance.search_unified(request)
         return json.dumps(result.model_dump(), ensure_ascii=False, indent=2)
         
-    except Exception:
+    except Exception as e:
-        logger.exception("Error in tool 'search_anayasa_unified'.")
+        logger.exception(f"Error in tool 'search_anayasa_unified'.")
         raise
 
 @app.tool(
@@ -1582,8 +1607,8 @@ async def get_anayasa_document_unified(
         result = await anayasa_unified_client_instance.get_document_unified(document_url, page_number)
         return json.dumps(result.model_dump(mode='json'), ensure_ascii=False, indent=2)
         
-    except Exception:
+    except Exception as e:
-        logger.exception("Error in tool 'get_anayasa_document_unified'.")
+        logger.exception(f"Error in tool 'get_anayasa_document_unified'.")
         raise
 
 # --- MCP Tools for KIK (Kamu İhale Kurulu) ---
@@ -1629,15 +1654,15 @@ async def search_kik_decisions(
         page=page
     )
     
-    logger.info("Tool 'search_kik_decisions' called.")
+    logger.info(f"Tool 'search_kik_decisions' called.")
     try:
         api_response = await kik_client_instance.search_decisions(search_query)
         page_param_for_log = search_query.page if hasattr(search_query, 'page') else 1
         if not api_response.decisions and api_response.total_records == 0 and page_param_for_log == 1:
-             logger.warning("KIK search returned no decisions for query.")
+             logger.warning(f"KIK search returned no decisions for query.")
         return api_response
-    except Exception:
+    except Exception as e:
-        logger.exception("Error in KIK search tool 'search_kik_decisions'.")
+        logger.exception(f"Error in KIK search tool 'search_kik_decisions'.")
         current_page_val = search_query.page if hasattr(search_query, 'page') else 1
         return KikSearchResult(decisions=[], total_records=0, current_page=current_page_val)
 
@@ -1733,7 +1758,7 @@ async def search_rekabet_kurumu_decisions(
     try:
        
         return await rekabet_client_instance.search_decisions(search_query)
-    except Exception:
+    except Exception as e:
         logger.exception("Error in tool 'search_rekabet_kurumu_decisions'.")
         return RekabetSearchResult(decisions=[], retrieved_page_number=page, total_records_found=0, total_pages=0)
 
@@ -1756,7 +1781,7 @@ async def get_rekabet_kurumu_document(
     try:
       
         return await rekabet_client_instance.get_decision_document(karar_id, page_number=current_page_to_fetch)
-    except Exception:
+    except Exception as e:
         logger.exception(f"Error in tool 'get_rekabet_kurumu_document'. Karar ID: {karar_id}")
         raise 
 
@@ -1851,7 +1876,7 @@ For best results, use exact phrases with quotes for legal terms."""),
             "page_size": pageSize,
             "searched_courts": court_types
         }
-    except Exception:
+    except Exception as e:
         logger.exception("Error in tool 'search_bedesten_unified'")
         raise
 
@@ -1873,7 +1898,7 @@ async def get_bedesten_document_markdown(
     
     try:
         return await bedesten_client_instance.get_document_as_markdown(documentId)
-    except Exception:
+    except Exception as e:
         logger.exception("Error in tool 'get_kyb_bedesten_document_markdown'")
         raise
 
@@ -2062,7 +2087,7 @@ async def search_sayistay_unified(
             web_karar_metni=web_karar_metni
         )
         return await sayistay_unified_client_instance.search_unified(search_request)
-    except Exception:
+    except Exception as e:
         logger.exception("Error in tool 'search_sayistay_unified'")
         raise
 
@@ -2086,7 +2111,7 @@ async def get_sayistay_document_unified(
     
     try:
         return await sayistay_unified_client_instance.get_document_unified(decision_id, decision_type)
-    except Exception:
+    except Exception as e:
         logger.exception("Error in tool 'get_sayistay_document_unified'")
         raise
 
@@ -2673,7 +2698,7 @@ async def search(
         logger.info(f"ChatGPT Deep Research search completed. Found {len(results)} results via Bedesten API.")
         return {"results": results}
         
-    except Exception:
+    except Exception as e:
         logger.exception("Error in ChatGPT Deep Research search tool")
         # Return partial results if any were found
         if results:
@@ -2802,7 +2827,7 @@ async def fetch(
             doc = await bedesten_client_instance.get_document_as_markdown(doc_id)
         """
         
-    except Exception:
+    except Exception as e:
         logger.exception(f"Error fetching ChatGPT Deep Research document {id}")
         raise
 
@@ -2849,7 +2874,7 @@ def main():
         app.run()
     except KeyboardInterrupt: 
         logger.info("Server shut down by user (KeyboardInterrupt).")
-    except Exception: 
+    except Exception as e: 
         logger.exception("Server failed to start or crashed.")
     finally:
         logger.info(f"{app.name} server has shut down.")

saidsurucu-yargi-mcp-f5fa007/redis_session_store.py

@@ -11,8 +11,8 @@ import os
 import json
 import time
 import logging
-from typing import Optional, Dict, Any
+from typing import Optional, Dict, Any, Union
-from datetime import datetime
+from datetime import datetime, timedelta
 
 logger = logging.getLogger(__name__)
 

saidsurucu-yargi-mcp-f5fa007/rekabet_mcp_module/client.py

@@ -4,9 +4,10 @@ import httpx
 from bs4 import BeautifulSoup
 from typing import List, Optional, Tuple, Dict, Any
 import logging
+import html
 import re
 import io # For io.BytesIO
-from urllib.parse import urljoin, parse_qs, urlparse
+from urllib.parse import urlencode, urljoin, quote, parse_qs, urlparse
 from markitdown import MarkItDown
 import math
 
@@ -17,7 +18,8 @@ from .models import (
     RekabetKurumuSearchRequest,
     RekabetDecisionSummary,
     RekabetSearchResult,
-    RekabetDocument
+    RekabetDocument,
+    RekabetKararTuruGuidEnum
 )
 from pydantic import HttpUrl # Ensure HttpUrl is imported from pydantic
 

saidsurucu-yargi-mcp-f5fa007/rekabet_mcp_module/models.py

@@ -1,7 +1,7 @@
 # rekabet_mcp_module/models.py
 
 from pydantic import BaseModel, Field, HttpUrl
-from typing import List, Optional
+from typing import List, Optional, Any
 from enum import Enum
 
 # Enum for decision type GUIDs (used by the client and expected by the website)

saidsurucu-yargi-mcp-f5fa007/run_asgi.py

@@ -93,7 +93,7 @@ def main():
         config["workers"] = args.workers
     
     # Print startup information
-    print("Starting Yargı MCP server...")
+    print(f"Starting Yargı MCP server...")
     print(f"Host: {args.host}")
     print(f"Port: {args.port}")
     print(f"Transport: {args.transport}")

saidsurucu-yargi-mcp-f5fa007/sayistay_mcp_module/client.py

@@ -1,11 +1,13 @@
 # sayistay_mcp_module/client.py
 
 import httpx
+import re
 from bs4 import BeautifulSoup
-from typing import Dict, List, Optional, Tuple
+from typing import Dict, Any, List, Optional, Tuple
 import logging
+import html
 import io
-from urllib.parse import urlencode
+from urllib.parse import urlencode, urljoin
 from markitdown import MarkItDown
 
 from .models import (
@@ -14,7 +16,7 @@ from .models import (
     DaireSearchRequest, DaireSearchResponse, DaireDecision,
     SayistayDocumentMarkdown
 )
-from .enums import WEB_KARAR_KONUSU_MAPPING
+from .enums import DaireEnum, KamuIdaresiTuruEnum, WebKararKonusuEnum, WEB_KARAR_KONUSU_MAPPING
 
 logger = logging.getLogger(__name__)
 if not logger.hasHandlers():

saidsurucu-yargi-mcp-f5fa007/sayistay_mcp_module/models.py

@@ -1,7 +1,7 @@
 # sayistay_mcp_module/models.py
 
 from pydantic import BaseModel, Field
-from typing import Optional, List, Dict, Any, Literal
+from typing import Optional, List, Union, Dict, Any, Literal
 from enum import Enum
 from .enums import DaireEnum, KamuIdaresiTuruEnum, WebKararKonusuEnum
 

saidsurucu-yargi-mcp-f5fa007/sayistay_mcp_module/unified_client.py

@@ -2,6 +2,8 @@
 # Unified client for all three Sayıştay decision types
 
 import logging
+from typing import Optional, Dict, Any
+from urllib.parse import urlparse
 
 from .models import (
     SayistayUnifiedSearchRequest,

saidsurucu-yargi-mcp-f5fa007/starlette_app.py

@@ -13,7 +13,7 @@ import os
 from starlette.applications import Starlette
 from starlette.routing import Mount, Route
 from starlette.requests import Request
-from starlette.responses import JSONResponse
+from starlette.responses import JSONResponse, PlainTextResponse, RedirectResponse
 from starlette.middleware import Middleware
 from starlette.middleware.cors import CORSMiddleware
 from starlette.middleware.authentication import AuthenticationMiddleware

saidsurucu-yargi-mcp-f5fa007/stripe_webhook.py

@@ -1,5 +1,4 @@
-import os
+import os, stripe
-import stripe
 from clerk_backend_api import Clerk                         # Clerk backend SDK
 from fastapi import APIRouter, Request, HTTPException
 

saidsurucu-yargi-mcp-f5fa007/uyusmazlik_mcp_module/client.py

@@ -3,7 +3,7 @@
 import httpx 
 import aiohttp 
 from bs4 import BeautifulSoup
-from typing import List, Optional, Tuple 
+from typing import Dict, Any, List, Optional, Union, Tuple 
 import logging
 import html
 import re

saidsurucu-yargi-mcp-f5fa007/yargitay_mcp_module/client.py

@@ -1,16 +1,20 @@
 # yargitay_mcp_module/client.py
 
 import httpx
-from typing import Optional
+from bs4 import BeautifulSoup # Still needed for pre-processing HTML before markitdown
+from typing import Dict, Any, List, Optional
 import logging
 import html
+import re
 import io
 from markitdown import MarkItDown
 
 from .models import (
     YargitayDetailedSearchRequest,
     YargitayApiSearchResponse,      
-    YargitayDocumentMarkdown 
+    YargitayApiDecisionEntry,
+    YargitayDocumentMarkdown,     
+    CompactYargitaySearchResult 
 )
 
 logger = logging.getLogger(__name__)

saidsurucu-yargi-mcp-f5fa007/yargitay_mcp_module/models.py

@@ -1,7 +1,7 @@
 # yargitay_mcp_module/models.py
 
 from pydantic import BaseModel, Field, HttpUrl, ConfigDict
-from typing import List, Optional, Literal
+from typing import List, Optional, Dict, Any, Literal
 
 # Yargıtay Chamber/Board Options
 YargitayBirimEnum = Literal[

sayistay_mcp_module/client.py

@@ -1,11 +1,13 @@
 # sayistay_mcp_module/client.py
 
 import httpx
+import re
 from bs4 import BeautifulSoup
-from typing import Dict, List, Optional, Tuple
+from typing import Dict, Any, List, Optional, Tuple
 import logging
+import html
 import io
-from urllib.parse import urlencode
+from urllib.parse import urlencode, urljoin
 from markitdown import MarkItDown
 
 from .models import (
@@ -14,7 +16,7 @@ from .models import (
     DaireSearchRequest, DaireSearchResponse, DaireDecision,
     SayistayDocumentMarkdown
 )
-from .enums import WEB_KARAR_KONUSU_MAPPING
+from .enums import DaireEnum, KamuIdaresiTuruEnum, WebKararKonusuEnum, WEB_KARAR_KONUSU_MAPPING
 
 logger = logging.getLogger(__name__)
 if not logger.hasHandlers():

sayistay_mcp_module/models.py

@@ -1,7 +1,7 @@
 # sayistay_mcp_module/models.py
 
 from pydantic import BaseModel, Field
-from typing import Optional, List, Dict, Any, Literal
+from typing import Optional, List, Union, Dict, Any, Literal
 from enum import Enum
 from .enums import DaireEnum, KamuIdaresiTuruEnum, WebKararKonusuEnum
 

sayistay_mcp_module/unified_client.py

@@ -2,6 +2,8 @@
 # Unified client for all three Sayıştay decision types
 
 import logging
+from typing import Optional, Dict, Any
+from urllib.parse import urlparse
 
 from .models import (
     SayistayUnifiedSearchRequest,

semantic_search/__init__.py

@@ -1,7 +0,0 @@
-# semantic_search/__init__.py
-
-from .embedder import EmbeddingGemma
-from .vector_store import VectorStore
-from .processor import DocumentProcessor
-
-__all__ = ['EmbeddingGemma', 'VectorStore', 'DocumentProcessor']

semantic_search/embedder.py

@@ -1,173 +0,0 @@
-# semantic_search/embedder.py
-
-import logging
-from typing import List, Optional
-import numpy as np
-from sentence_transformers import SentenceTransformer
-import torch
-
-logger = logging.getLogger(__name__)
-
-class EmbeddingGemma:
-    """
-    Wrapper for Google's EmbeddingGemma model.
-    Handles query and document encoding with proper prompt templates.
-    """
-    
-    def __init__(self, model_name: str = "google/embeddinggemma-300m", device: Optional[str] = None):
-        """
-        Initialize EmbeddingGemma model.
-        
-        Args:
-            model_name: HuggingFace model name
-            device: Device to run model on ('cuda', 'cpu', or None for auto)
-        """
-        self.model_name = model_name
-        
-        # Auto-detect device if not specified
-        if device is None:
-            self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
-        else:
-            self.device = device
-            
-        logger.info(f"Initializing EmbeddingGemma on device: {self.device}")
-        
-        try:
-            # Load model with float32 precision (EmbeddingGemma doesn't support float16)
-            self.model = SentenceTransformer(model_name, device=self.device)
-            self.model.eval()  # Set to evaluation mode
-            
-            # Set precision to float32 or bfloat16
-            if self.device == 'cuda' and torch.cuda.is_bf16_supported():
-                logger.info("Using bfloat16 precision for CUDA")
-                self.dtype = torch.bfloat16
-            else:
-                logger.info("Using float32 precision")
-                self.dtype = torch.float32
-                
-            logger.info(f"Successfully loaded model: {model_name}")
-            
-        except Exception as e:
-            logger.error(f"Failed to load EmbeddingGemma model: {e}")
-            raise
-    
-    def encode_query(self, query: str, task: str = "search result") -> np.ndarray:
-        """
-        Encode a search query with appropriate prompt template.
-        
-        Args:
-            query: The search query text
-            task: Task type for prompt template (search result, question answering, etc.)
-            
-        Returns:
-            Numpy array of embeddings (768 dimensions)
-        """
-        # Apply query prompt template
-        prompted_query = f"task: {task} | query: {query}"
-        
-        try:
-            with torch.no_grad():
-                # Encode with model
-                embeddings = self.model.encode(
-                    prompted_query,
-                    convert_to_numpy=True,
-                    normalize_embeddings=True,  # L2 normalization for cosine similarity
-                    show_progress_bar=False
-                )
-            
-            logger.debug(f"Encoded query: {query[:50]}... -> shape: {embeddings.shape}")
-            return embeddings
-            
-        except Exception as e:
-            logger.error(f"Failed to encode query: {e}")
-            raise
-    
-    def encode_documents(self, documents: List[str], titles: Optional[List[str]] = None) -> np.ndarray:
-        """
-        Encode multiple documents with appropriate prompt template.
-        
-        Args:
-            documents: List of document texts
-            titles: Optional list of document titles
-            
-        Returns:
-            Numpy array of embeddings (N x 768 dimensions)
-        """
-        if not documents:
-            return np.array([])
-        
-        # Apply document prompt template
-        prompted_docs = []
-        for i, doc in enumerate(documents):
-            title = titles[i] if titles and i < len(titles) else "none"
-            prompted_doc = f"title: {title} | text: {doc}"
-            prompted_docs.append(prompted_doc)
-        
-        try:
-            with torch.no_grad():
-                # Batch encode documents
-                embeddings = self.model.encode(
-                    prompted_docs,
-                    convert_to_numpy=True,
-                    normalize_embeddings=True,
-                    show_progress_bar=len(documents) > 10,
-                    batch_size=8  # Adjust based on memory
-                )
-            
-            logger.info(f"Encoded {len(documents)} documents -> shape: {embeddings.shape}")
-            return embeddings
-            
-        except Exception as e:
-            logger.error(f"Failed to encode documents: {e}")
-            raise
-    
-    def reduce_dimensions(self, embeddings: np.ndarray, target_dim: int = 512) -> np.ndarray:
-        """
-        Reduce embedding dimensions using Matryoshka Representation Learning.
-        
-        Args:
-            embeddings: Original embeddings (N x 768)
-            target_dim: Target dimension (512, 256, or 128)
-            
-        Returns:
-            Reduced embeddings (N x target_dim)
-        """
-        if target_dim not in [512, 256, 128]:
-            raise ValueError(f"Target dimension must be 512, 256, or 128, got {target_dim}")
-        
-        if len(embeddings.shape) == 1:
-            # Single embedding
-            reduced = embeddings[:target_dim]
-            # Re-normalize after truncation
-            norm = np.linalg.norm(reduced)
-            if norm > 0:
-                reduced = reduced / norm
-        else:
-            # Multiple embeddings
-            reduced = embeddings[:, :target_dim]
-            # Re-normalize each embedding
-            norms = np.linalg.norm(reduced, axis=1, keepdims=True)
-            reduced = reduced / (norms + 1e-8)  # Avoid division by zero
-        
-        logger.debug(f"Reduced dimensions: {embeddings.shape} -> {reduced.shape}")
-        return reduced
-    
-    def compute_similarity(self, query_embedding: np.ndarray, document_embeddings: np.ndarray) -> np.ndarray:
-        """
-        Compute cosine similarity between query and documents.
-        
-        Args:
-            query_embedding: Query embedding (768,)
-            document_embeddings: Document embeddings (N x 768)
-            
-        Returns:
-            Similarity scores (N,)
-        """
-        # Ensure query is 2D for matrix multiplication
-        if len(query_embedding.shape) == 1:
-            query_embedding = query_embedding.reshape(1, -1)
-        
-        # Compute cosine similarity (embeddings are already normalized)
-        similarities = np.dot(document_embeddings, query_embedding.T).squeeze()
-        
-        return similarities

semantic_search/processor.py

@@ -1,305 +0,0 @@
-# semantic_search/processor.py
-
-import logging
-import re
-from typing import List, Dict, Any, Optional
-from dataclasses import dataclass
-import hashlib
-
-logger = logging.getLogger(__name__)
-
-@dataclass
-class DocumentChunk:
-    """Represents a chunk of a document."""
-    chunk_id: str
-    document_id: str
-    text: str
-    metadata: Dict[str, Any]
-    chunk_index: int
-    total_chunks: int
-
-class DocumentProcessor:
-    """
-    Processes legal documents for semantic search.
-    Handles chunking, cleaning, and metadata extraction.
-    """
-    
-    def __init__(self, 
-                 chunk_size: int = 1000,
-                 chunk_overlap: int = 200,
-                 min_chunk_size: int = 100):
-        """
-        Initialize document processor.
-        
-        Args:
-            chunk_size: Target size for each chunk in characters
-            chunk_overlap: Number of overlapping characters between chunks
-            min_chunk_size: Minimum chunk size to keep
-        """
-        self.chunk_size = chunk_size
-        self.chunk_overlap = chunk_overlap
-        self.min_chunk_size = min_chunk_size
-        
-        logger.info(f"Initialized DocumentProcessor (chunk_size={chunk_size}, overlap={chunk_overlap})")
-    
-    def process_document(self, 
-                        document_id: str,
-                        text: str,
-                        metadata: Optional[Dict[str, Any]] = None) -> List[DocumentChunk]:
-        """
-        Process a single document into chunks.
-        
-        Args:
-            document_id: Unique document identifier
-            text: Document text content
-            metadata: Optional document metadata
-            
-        Returns:
-            List of document chunks
-        """
-        if not text or len(text.strip()) < self.min_chunk_size:
-            logger.warning(f"Document {document_id} too short to process")
-            return []
-        
-        # Clean text
-        cleaned_text = self._clean_text(text)
-        
-        # Extract metadata from text if not provided
-        if metadata is None:
-            metadata = {}
-        
-        # Add extracted metadata
-        extracted_metadata = self._extract_metadata(cleaned_text)
-        metadata.update(extracted_metadata)
-        
-        # Create chunks
-        chunks = self._create_chunks(cleaned_text)
-        
-        # Create DocumentChunk objects
-        document_chunks = []
-        for i, chunk_text in enumerate(chunks):
-            chunk_id = self._generate_chunk_id(document_id, i)
-            
-            chunk = DocumentChunk(
-                chunk_id=chunk_id,
-                document_id=document_id,
-                text=chunk_text,
-                metadata={
-                    **metadata,
-                    'chunk_index': i,
-                    'total_chunks': len(chunks)
-                },
-                chunk_index=i,
-                total_chunks=len(chunks)
-            )
-            document_chunks.append(chunk)
-        
-        logger.info(f"Processed document {document_id} into {len(chunks)} chunks")
-        return document_chunks
-    
-    def _clean_text(self, text: str) -> str:
-        """
-        Clean and normalize text for processing.
-        
-        Args:
-            text: Raw text
-            
-        Returns:
-            Cleaned text
-        """
-        # Remove excessive whitespace
-        text = re.sub(r'\s+', ' ', text)
-        
-        # Remove special characters but keep Turkish characters
-        # Keep: letters, numbers, spaces, and common punctuation
-        text = re.sub(r'[^\w\s\.\,\;\:\!\?\-\(\)\"\'ÇĞIİÖŞÜçğıiöşü]', ' ', text)
-        
-        # Remove multiple spaces
-        text = re.sub(r' +', ' ', text)
-        
-        # Trim
-        text = text.strip()
-        
-        return text
-    
-    def _extract_metadata(self, text: str) -> Dict[str, Any]:
-        """
-        Extract metadata from legal document text.
-        
-        Args:
-            text: Document text
-            
-        Returns:
-            Extracted metadata
-        """
-        metadata = {}
-        
-        # Extract case numbers (Esas/Karar)
-        esas_pattern = r'E(?:sas)?[\s\.\:]*(\d{4})[\/\-](\d+)'
-        karar_pattern = r'K(?:arar)?[\s\.\:]*(\d{4})[\/\-](\d+)'
-        
-        esas_match = re.search(esas_pattern, text[:500])  # Look in first 500 chars
-        if esas_match:
-            metadata['esas_no'] = f"E.{esas_match.group(1)}/{esas_match.group(2)}"
-        
-        karar_match = re.search(karar_pattern, text[:500])
-        if karar_match:
-            metadata['karar_no'] = f"K.{karar_match.group(1)}/{karar_match.group(2)}"
-        
-        # Extract dates (DD.MM.YYYY or DD/MM/YYYY format)
-        date_pattern = r'(\d{1,2})[\.\/](\d{1,2})[\.\/](\d{4})'
-        dates = re.findall(date_pattern, text[:1000])  # Look in first 1000 chars
-        if dates:
-            # Take the first date as decision date
-            day, month, year = dates[0]
-            metadata['karar_tarihi'] = f"{year}-{month.zfill(2)}-{day.zfill(2)}"
-        
-        # Extract court/chamber name
-        chamber_patterns = [
-            r'(\d+)\.\s*Hukuk\s+Dairesi',
-            r'(\d+)\.\s*Ceza\s+Dairesi',
-            r'Hukuk\s+Genel\s+Kurulu',
-            r'Ceza\s+Genel\s+Kurulu',
-            r'(\d+)\.\s*Daire'
-        ]
-        
-        for pattern in chamber_patterns:
-            match = re.search(pattern, text[:500], re.IGNORECASE)
-            if match:
-                metadata['chamber'] = match.group(0)
-                break
-        
-        return metadata
-    
-    def _create_chunks(self, text: str) -> List[str]:
-        """
-        Create overlapping chunks from text.
-        
-        Args:
-            text: Cleaned document text
-            
-        Returns:
-            List of text chunks
-        """
-        chunks = []
-        
-        # Split by sentences for better semantic coherence
-        sentences = self._split_sentences(text)
-        
-        current_chunk = []
-        current_size = 0
-        
-        for sentence in sentences:
-            sentence_size = len(sentence)
-            
-            # If adding this sentence exceeds chunk size
-            if current_size + sentence_size > self.chunk_size and current_chunk:
-                # Save current chunk
-                chunk_text = ' '.join(current_chunk)
-                chunks.append(chunk_text)
-                
-                # Create overlap for next chunk
-                overlap_size = 0
-                overlap_sentences = []
-                
-                # Add sentences from the end until we reach overlap size
-                for sent in reversed(current_chunk):
-                    overlap_size += len(sent)
-                    overlap_sentences.insert(0, sent)
-                    if overlap_size >= self.chunk_overlap:
-                        break
-                
-                # Start new chunk with overlap
-                current_chunk = overlap_sentences
-                current_size = sum(len(s) for s in current_chunk)
-            
-            # Add sentence to current chunk
-            current_chunk.append(sentence)
-            current_size += sentence_size
-        
-        # Add final chunk if not empty
-        if current_chunk:
-            chunk_text = ' '.join(current_chunk)
-            if len(chunk_text) >= self.min_chunk_size:
-                chunks.append(chunk_text)
-        
-        return chunks
-    
-    def _split_sentences(self, text: str) -> List[str]:
-        """
-        Split text into sentences.
-        
-        Args:
-            text: Text to split
-            
-        Returns:
-            List of sentences
-        """
-        # Simple sentence splitting for Turkish text
-        # Split on period, question mark, exclamation, but not on abbreviations
-        
-        # Common Turkish abbreviations to preserve
-        abbreviations = ['Dr', 'Prof', 'Av', 'Md', 'Yrd', 'Doç', 'No', 'S', 'vs', 'vb', 'bkz']
-        
-        # Replace abbreviations temporarily
-        temp_text = text
-        replacements = {}
-        for i, abbr in enumerate(abbreviations):
-            placeholder = f"__ABBR{i}__"
-            temp_text = temp_text.replace(f"{abbr}.", placeholder)
-            replacements[placeholder] = f"{abbr}."
-        
-        # Split sentences
-        sentence_endings = re.compile(r'[.!?]+')
-        sentences = sentence_endings.split(temp_text)
-        
-        # Restore abbreviations and clean
-        cleaned_sentences = []
-        for sentence in sentences:
-            # Restore abbreviations
-            for placeholder, original in replacements.items():
-                sentence = sentence.replace(placeholder, original)
-            
-            # Clean and add if not empty
-            sentence = sentence.strip()
-            if sentence and len(sentence) > 10:  # Minimum sentence length
-                cleaned_sentences.append(sentence)
-        
-        return cleaned_sentences
-    
-    def _generate_chunk_id(self, document_id: str, chunk_index: int) -> str:
-        """
-        Generate unique chunk ID.
-        
-        Args:
-            document_id: Parent document ID
-            chunk_index: Index of chunk in document
-            
-        Returns:
-            Unique chunk ID
-        """
-        chunk_string = f"{document_id}_chunk_{chunk_index}"
-        chunk_hash = hashlib.md5(chunk_string.encode()).hexdigest()[:8]
-        return f"{document_id}_c{chunk_index}_{chunk_hash}"
-    
-    def combine_chunks(self, chunks: List[DocumentChunk]) -> str:
-        """
-        Combine chunks back into full document text.
-        
-        Args:
-            chunks: List of document chunks
-            
-        Returns:
-            Combined text
-        """
-        if not chunks:
-            return ""
-        
-        # Sort by chunk index
-        sorted_chunks = sorted(chunks, key=lambda x: x.chunk_index)
-        
-        # For overlapping chunks, we need to be careful about duplication
-        # Simple approach: just concatenate with space
-        combined = " ".join([chunk.text for chunk in sorted_chunks])
-        
-        return combined

semantic_search/vector_store.py

@@ -1,235 +0,0 @@
-# semantic_search/vector_store.py
-
-import logging
-import numpy as np
-from typing import List, Dict, Any, Tuple, Optional
-from dataclasses import dataclass
-import json
-
-logger = logging.getLogger(__name__)
-
-@dataclass
-class Document:
-    """Represents a document with its embedding and metadata."""
-    id: str
-    text: str
-    embedding: np.ndarray
-    metadata: Dict[str, Any]
-    
-    def to_dict(self) -> Dict[str, Any]:
-        """Convert to dictionary (excluding embedding for serialization)."""
-        return {
-            'id': self.id,
-            'text': self.text,
-            'metadata': self.metadata
-        }
-
-class VectorStore:
-    """
-    In-memory vector storage with similarity search capabilities.
-    Future versions can use Faiss, ChromaDB, or other vector databases.
-    """
-    
-    def __init__(self, dimension: int = 768):
-        """
-        Initialize vector store.
-        
-        Args:
-            dimension: Embedding dimension size
-        """
-        self.dimension = dimension
-        self.documents: List[Document] = []
-        self.embeddings: Optional[np.ndarray] = None
-        self.index_built = False
-        
-        logger.info(f"Initialized VectorStore with dimension: {dimension}")
-    
-    def add_documents(self, 
-                     ids: List[str],
-                     texts: List[str],
-                     embeddings: np.ndarray,
-                     metadata: Optional[List[Dict[str, Any]]] = None) -> int:
-        """
-        Add documents to the vector store.
-        
-        Args:
-            ids: Document IDs
-            texts: Document texts
-            embeddings: Document embeddings (N x dimension)
-            metadata: Optional metadata for each document
-            
-        Returns:
-            Number of documents added
-        """
-        if len(ids) != len(texts) or len(ids) != embeddings.shape[0]:
-            raise ValueError("Mismatched lengths for ids, texts, and embeddings")
-        
-        if metadata and len(metadata) != len(ids):
-            raise ValueError("Metadata length doesn't match document count")
-        
-        # Add documents
-        for i in range(len(ids)):
-            doc = Document(
-                id=ids[i],
-                text=texts[i],
-                embedding=embeddings[i],
-                metadata=metadata[i] if metadata else {}
-            )
-            self.documents.append(doc)
-        
-        # Rebuild index
-        self._build_index()
-        
-        logger.info(f"Added {len(ids)} documents to vector store. Total: {len(self.documents)}")
-        return len(ids)
-    
-    def _build_index(self):
-        """Build or rebuild the embedding index."""
-        if not self.documents:
-            self.embeddings = None
-            self.index_built = False
-            return
-        
-        # Stack all embeddings into a single array
-        self.embeddings = np.vstack([doc.embedding for doc in self.documents])
-        self.index_built = True
-        
-        logger.debug(f"Built index with shape: {self.embeddings.shape}")
-    
-    def search(self, 
-              query_embedding: np.ndarray,
-              top_k: int = 10,
-              threshold: Optional[float] = None) -> List[Tuple[Document, float]]:
-        """
-        Search for similar documents using cosine similarity.
-        
-        Args:
-            query_embedding: Query embedding vector
-            top_k: Number of results to return
-            threshold: Optional similarity threshold (0-1)
-            
-        Returns:
-            List of (Document, similarity_score) tuples
-        """
-        if not self.index_built or self.embeddings is None:
-            logger.warning("No documents in vector store")
-            return []
-        
-        # Ensure query is 2D
-        if len(query_embedding.shape) == 1:
-            query_embedding = query_embedding.reshape(1, -1)
-        
-        # Compute cosine similarities (assuming normalized embeddings)
-        similarities = np.dot(self.embeddings, query_embedding.T).squeeze()
-        
-        # Apply threshold if specified
-        if threshold is not None:
-            valid_indices = np.where(similarities >= threshold)[0]
-            if len(valid_indices) == 0:
-                logger.info(f"No documents above threshold {threshold}")
-                return []
-            similarities = similarities[valid_indices]
-            valid_docs = [self.documents[i] for i in valid_indices]
-        else:
-            valid_docs = self.documents
-        
-        # Get top-k indices
-        top_k = min(top_k, len(valid_docs))
-        if top_k == 0:
-            return []
-        
-        # Use argpartition for efficiency with large arrays
-        if len(similarities) > top_k:
-            top_indices = np.argpartition(similarities, -top_k)[-top_k:]
-            top_indices = top_indices[np.argsort(similarities[top_indices])[::-1]]
-        else:
-            top_indices = np.argsort(similarities)[::-1]
-        
-        # Create results
-        results = []
-        for idx in top_indices:
-            doc = valid_docs[idx] if threshold else self.documents[idx]
-            score = float(similarities[idx])
-            results.append((doc, score))
-        
-        logger.info(f"Search returned {len(results)} results (top_k={top_k})")
-        return results
-    
-    def hybrid_search(self,
-                     query_embedding: np.ndarray,
-                     keyword_scores: Dict[str, float],
-                     top_k: int = 10,
-                     alpha: float = 0.5) -> List[Tuple[Document, float]]:
-        """
-        Hybrid search combining vector similarity and keyword scores.
-        
-        Args:
-            query_embedding: Query embedding vector
-            keyword_scores: Document ID to keyword relevance score mapping
-            top_k: Number of results to return
-            alpha: Weight for vector similarity (1-alpha for keyword score)
-            
-        Returns:
-            List of (Document, combined_score) tuples
-        """
-        if not self.index_built:
-            logger.warning("No documents in vector store")
-            return []
-        
-        # Get vector similarities
-        vector_results = self.search(query_embedding, top_k=len(self.documents))
-        
-        # Combine scores
-        combined_scores = []
-        for doc, vector_score in vector_results:
-            keyword_score = keyword_scores.get(doc.id, 0.0)
-            # Normalize keyword score to 0-1 range if needed
-            if keyword_score > 1.0:
-                keyword_score = keyword_score / max(keyword_scores.values())
-            
-            combined_score = alpha * vector_score + (1 - alpha) * keyword_score
-            combined_scores.append((doc, combined_score))
-        
-        # Sort by combined score and return top-k
-        combined_scores.sort(key=lambda x: x[1], reverse=True)
-        results = combined_scores[:top_k]
-        
-        logger.info(f"Hybrid search returned {len(results)} results")
-        return results
-    
-    def clear(self):
-        """Clear all documents from the store."""
-        self.documents = []
-        self.embeddings = None
-        self.index_built = False
-        logger.info("Cleared vector store")
-    
-    def size(self) -> int:
-        """Get number of documents in store."""
-        return len(self.documents)
-    
-    def get_by_id(self, doc_id: str) -> Optional[Document]:
-        """Get document by ID."""
-        for doc in self.documents:
-            if doc.id == doc_id:
-                return doc
-        return None
-    
-    def get_stats(self) -> Dict[str, Any]:
-        """Get statistics about the vector store."""
-        stats = {
-            'num_documents': len(self.documents),
-            'dimension': self.dimension,
-            'index_built': self.index_built,
-            'memory_usage_mb': 0
-        }
-        
-        if self.embeddings is not None:
-            # Estimate memory usage
-            memory_bytes = self.embeddings.nbytes
-            for doc in self.documents:
-                memory_bytes += len(doc.text.encode('utf-8'))
-                memory_bytes += len(json.dumps(doc.metadata).encode('utf-8'))
-            stats['memory_usage_mb'] = memory_bytes / (1024 * 1024)
-        
-        return stats

starlette_app.py

@@ -13,7 +13,7 @@ import os
 from starlette.applications import Starlette
 from starlette.routing import Mount, Route
 from starlette.requests import Request
-from starlette.responses import JSONResponse
+from starlette.responses import JSONResponse, PlainTextResponse, RedirectResponse
 from starlette.middleware import Middleware
 from starlette.middleware.cors import CORSMiddleware
 from starlette.middleware.authentication import AuthenticationMiddleware

stripe_webhook.py

@@ -1,5 +1,4 @@
-import os
+import os, stripe
-import stripe
 from clerk_backend_api import Clerk                         # Clerk backend SDK
 from fastapi import APIRouter, Request, HTTPException
 

uyusmazlik_mcp_module/client.py

@@ -2,7 +2,7 @@
 
 import httpx 
 from bs4 import BeautifulSoup
-from typing import List, Optional, Tuple 
+from typing import Dict, Any, List, Optional, Union, Tuple 
 import logging
 import html
 import re

yargitay_mcp_module/client.py

@@ -1,16 +1,20 @@
 # yargitay_mcp_module/client.py
 
 import httpx
-from typing import Optional
+from bs4 import BeautifulSoup # Still needed for pre-processing HTML before markitdown
+from typing import Dict, Any, List, Optional
 import logging
 import html
+import re
 import io
 from markitdown import MarkItDown
 
 from .models import (
     YargitayDetailedSearchRequest,
     YargitayApiSearchResponse,      
-    YargitayDocumentMarkdown 
+    YargitayApiDecisionEntry,
+    YargitayDocumentMarkdown,     
+    CompactYargitaySearchResult 
 )
 
 logger = logging.getLogger(__name__)

yargitay_mcp_module/models.py

@@ -1,7 +1,7 @@
 # yargitay_mcp_module/models.py
 
 from pydantic import BaseModel, Field, HttpUrl, ConfigDict
-from typing import List, Optional, Literal
+from typing import List, Optional, Dict, Any, Literal
 
 # Yargıtay Chamber/Board Options
 YargitayBirimEnum = Literal[