Fix: Correct authentication and add ping method

.env.example

@@ -1,32 +0,0 @@
-# MCP Summary Server - Environment Variables
-
-# Server Configuration
-PORT=8080
-
-# Authentication (optional)
-# If set, requests must include: Authorization: Bearer <API_KEY>
-API_KEY=
-
-# LLM Configuration
-OPENAPI_URL=http://localhost:8080/v1
-OPENAPI_API_KEY=
-MODEL_NAME=gpt-4o
-
-# LLM Call Timeout in seconds (increase for large documents)
-LLM_TIMEOUT=120
-
-# Summarization Configuration
-# Characters per chunk when splitting long text
-CHUNK_SIZE=4000
-
-# Characters of overlap between chunks to maintain context
-OVERLAP=200
-
-# Target length for intermediate chunk summaries (words)
-TARGET_INTERMEDIATE_SUMMARY_LENGTH=150
-
-# Maximum length for final synthesized summary (words)
-MAX_DIRECT_SUMMARY_LENGTH=100
-
-# Maximum text length (characters) before chunking is triggered
-MAX_DIRECT_TEXT_LENGTH=8000

Dockerfile

@@ -1,37 +0,0 @@
-# Dockerfile for MCP Summary Server
-#
-# Usage (from directory containing this Dockerfile and mcp_summary_server.py):
-#
-#   docker build -t mcp-summary .
-#   docker run -p 8080:8080 --env-file .env mcp-summary
-#
-
-FROM python:3.12-slim
-
-WORKDIR /app
-
-# Install runtime dependencies
-COPY requirements.txt .
-RUN pip install --no-cache-dir -r requirements.txt && rm requirements.txt
-
-# Copy the server script
-COPY mcp_summary_server.py /app/mcp_summary_server.py
-
-# Expose HTTP port
-EXPOSE 8080
-
-# Environment variables
-ENV PORT=8080
-ENV OPENAPI_URL=http://localhost:8080/v1
-ENV OPENAPI_API_KEY=
-ENV MODEL_NAME=gpt-4o
-ENV CHUNK_SIZE=4000
-ENV OVERLAP=200
-ENV TARGET_INTERMEDIATE_SUMMARY_LENGTH=150
-ENV MAX_DIRECT_SUMMARY_LENGTH=100
-ENV MAX_DIRECT_TEXT_LENGTH=8000
-ENV LLM_TIMEOUT=120
-ENV API_KEY=
-
-# Start the MCP summary server
-ENTRYPOINT ["python", "-u", "/app/mcp_summary_server.py"]

README.md

@@ -1,137 +0,0 @@
-# MCP Summary Server
-
-An MCP (Model Context Protocol) server for document summarization that keeps full text out of the chat context window.
-
-## Features
-
-- Automatically determines whether to summarize directly or use chunked summarization
-- All processing happens server-side
-- Returns only the summary to the client
-- Configurable chunking parameters
-- Bearer token authentication (optional)
-
-## Setup
-
-### Environment Variables
-
-Copy `.env.example` to `.env` and configure:
-
-```bash
-cp .env.example .env
-```
-
-| Variable | Default | Description |
-|----------|---------|-------------|
-| PORT | 8080 | HTTP server port |
-| API_KEY | (empty) | Bearer token for authentication |
-| OPENAPI_URL | http://localhost:8080/v1 | LLM API endpoint |
-| OPENAPI_API_KEY | (empty) | LLM API key |
-| MODEL_NAME | gpt-4o | LLM model to use |
-| LLM_TIMEOUT | 120 | LLM call timeout in seconds |
-| CHUNK_SIZE | 4000 | Characters per chunk |
-| OVERLAP | 200 | Characters of overlap between chunks |
-| TARGET_INTERMEDIATE_SUMMARY_LENGTH | 150 | Words per chunk summary |
-| MAX_DIRECT_SUMMARY_LENGTH | 100 | Max final summary length |
-| MAX_DIRECT_TEXT_LENGTH | 8000 | Max text length before chunking |
-
-## Running
-
-### Docker
-
-```bash
-# Build
-docker build -t mcp-summary .
-
-# Run with environment file
-docker run -p 8080:8080 --env-file .env mcp-summary
-
-# Run with inline environment variables
-docker run -p 8080:8080 \
-  -e OPENAPI_URL=http://localhost:8080/v1 \
-  -e OPENAPI_API_KEY=your-key \
-  -e MODEL_NAME=gpt-4o \
-  mcp-summary
-```
-
-### Python
-
-```bash
-pip install -r requirements.txt
-python mcp_summary_server.py
-```
-
-## Connecting to OpenWebUI
-
-### In OpenWebUI Admin Settings
-
-1. Go to **Admin Settings → External Tools**
-2. Click **+ (Add Server)**
-3. Set **Type** to **MCP (Streamable HTTP)**
-4. Enter your **Server URL**
-5. Set **Authentication**:
-   - **None** if no API key is configured
-   - **Bearer** if API_KEY is set (provide the key)
-6. Save
-
-### Docker Networking
-
-If running both OpenWebUI and MCP Summary in Docker:
-
-```bash
-# Use host.docker.internal to reach host machine
-docker run -p 8080:8080 \
-  -e OPENAPI_URL=http://host.docker.internal:3000/v1 \
-  -e OPENAPI_API_KEY=your-key \
-  mcp-summary
-```
-
-If both containers are on the same Docker network, use the container name directly:
-
-```bash
-docker run --network mynetwork -p 8080:8080 \
-  -e OPENAPI_URL=http://openwebui-container:8080/v1 \
-  -e OPENAPI_API_KEY=your-key \
-  mcp-summary
-```
-
-## MCP Tool
-
-### summarize_document
-
-Summarizes a document, automatically handling chunking for long text.
-
-**Parameters:**
-- `text` (string, required): The document text to summarize
-- `max_length` (integer, optional): Maximum summary length in words (default: 100)
-
-**Returns:**
-```json
-{
-  "summary": "The summarized text...",
-  "original_length": 12345,
-  "method": "direct",  // or "chunked"
-  "chunks": 1  // number of chunks used
-}
-```
-
-## Troubleshooting
-
-### "Failed to connect to MCP server"
-
-1. **Check authentication**: Ensure you haven't selected `Bearer` without a key. Switch to `None` if no token is needed.
-2. **Check network connectivity**: Ensure OpenWebUI can reach the MCP server URL
-3. **Check LLM connectivity**: Ensure the MCP server can reach the LLM at OPENAPI_URL
-4. **Check timeouts**: Increase LLM_TIMEOUT if summarization takes too long
-
-### Infinite loading screen
-
-This may occur if you configured the server as OpenAPI instead of MCP. Fix by:
-
-1. Opening Admin Settings → External Tools
-2. Disabling/deleting the problematic connection
-3. Re-adding with **Type** set to **MCP (Streamable HTTP)**
-
-### Slow initialization
-
-If the server takes longer than 10 seconds to initialize:
-- Increase `MCP_INITIALIZE_TIMEOUT` in OpenWebUI (default: 10 seconds)

diagnose.sh

@@ -1,34 +0,0 @@
-#!/bin/bash
-# Diagnostic script for MCP Summary Server
-
-echo "================================"
-echo "MCP Summary Server Diagnostics"
-echo "================================"
-
-# Check if server is running
-echo -e "\n1. Checking if server process is running..."
-ps aux | grep mcp_summary_server || echo "Server process not found"
-
-# Check if port is listening
-echo -e "\n2. Checking if port is listening..."
-netstat -tlnp 2>/dev/null | grep 8080 || echo "Port 8080 not listening"
-
-# Test basic connectivity
-echo -e "\n3. Testing basic connectivity..."
-curl -s http://localhost:8080/ || echo "Cannot connect to localhost:8080"
-
-# Test MCP initialize
-echo -e "\n4. Testing MCP initialize..."
-curl -s -X POST http://localhost:8080/ \
-  -H "Content-Type: application/json" \
-  -d '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2025-11-25","capabilities":{},"clientInfo":{"name":"test","version":"1.0.0"}}}' | jq .
-
-# Test tools list
-echo -e "\n5. Testing tools list..."
-curl -s -X POST http://localhost:8080/ \
-  -H "Content-Type: application/json" \
-  -d '{"jsonrpc":"2.0","id":2,"method":"tools/list","params":{}}' | jq .
-
-echo -e "\n================================"
-echo "Diagnostics complete"
-echo "================================"

mcp_summary_server.py

@@ -24,11 +24,32 @@ Auth:
 import json
 import os
 import sys
+import logging
 from http.server import HTTPServer, BaseHTTPRequestHandler
-from typing import Any, Dict, Optional
+from typing import Any, Dict, List, Optional
 import requests
+from requests.exceptions import RequestException
 
+# Configure logging
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger("mcp-summary")
+
+# MCP Server Configuration
 API_KEY = os.environ.get("API_KEY", "").strip()
+PORT = int(os.environ.get("PORT", "8080"))
+
+# LLM Configuration
+OPENAPI_URL = os.environ.get("OPENAPI_URL", "http://localhost:8080/v1")
+OPENAPI_API_KEY = os.environ.get("OPENAPI_API_KEY", "")
+MODEL_NAME = os.environ.get("MODEL_NAME", "gpt-4o")
+
+# Summarization Configuration
+CHUNK_SIZE = int(os.environ.get("CHUNK_SIZE", "4000"))
+OVERLAP = int(os.environ.get("OVERLAP", "200"))
+TARGET_INTERMEDIATE_SUMMARY_LENGTH = int(os.environ.get("TARGET_INTERMEDIATE_SUMMARY_LENGTH", "150"))
+MAX_DIRECT_SUMMARY_LENGTH = int(os.environ.get("MAX_DIRECT_SUMMARY_LENGTH", "100"))
+MAX_DIRECT_TEXT_LENGTH = int(os.environ.get("MAX_DIRECT_TEXT_LENGTH", "8000"))
+LLM_TIMEOUT = int(os.environ.get("LLM_TIMEOUT", "120"))
 
 # Tool definitions
 TOOLS_LIST: Dict[str, Any] = {
@@ -64,8 +85,7 @@ def get_bearer_token(headers: Any) -> Optional[str]:
 
 
 def require_auth(headers: Any) -> bool:
-    """Check authentication if API key is configured."""
-    # If API_KEY is not set, allow unauthenticated access
+    """Check authentication. Returns True if auth passes or is not required."""
     if not API_KEY:
         return True
 
@@ -75,55 +95,52 @@ def require_auth(headers: Any) -> bool:
     return True
 
 
-def call_llm(text: str, system_prompt: str, max_tokens: int = 2000) -> str:
-    """Make an OpenAPI-compatible LLM call."""
-    openapi_url = os.environ.get("OPENAPI_URL", "http://localhost:8080/v1")
-    openapi_api_key = os.environ.get("OPENAPI_API_KEY", "")
-    model_name = os.environ.get("MODEL_NAME", "gpt-4o")
-    timeout = int(os.environ.get("LLM_TIMEOUT", "120"))
-    
-    url = f"{openapi_url}/chat/completions"
+def call_llm(messages: List[Dict], temperature: float = 0.3) -> str:
+    """Make an OpenAPI-compatible LLM call with error handling."""
+    url = f"{OPENAPI_URL}/chat/completions"
     headers = {
         "Content-Type": "application/json",
-        "Authorization": f"Bearer {openapi_api_key}"
+        "Authorization": f"Bearer {OPENAPI_API_KEY}"
     }
     
     payload = {
-        "model": model_name,
-        "messages": [
-            {"role": "system", "content": system_prompt},
-            {"role": "user", "content": text}
-        ],
-        "temperature": 0.3,
-        "max_tokens": max_tokens,
+        "model": MODEL_NAME,
+        "messages": messages,
+        "temperature": temperature,
+        "max_tokens": 2000,
         "top_p": 0.9
     }
     
-    response = requests.post(url, headers=headers, json=payload, timeout=timeout)
-    response.raise_for_status()
+    try:
+        logger.info(f"Calling LLM at {OPENAPI_URL} with model {MODEL_NAME}")
+        response = requests.post(url, headers=headers, json=payload, timeout=LLM_TIMEOUT)
+        response.raise_for_status()
         
-    data = response.json()
-    return data["choices"][0]["message"]["content"]
+        data = response.json()
+        return data["choices"][0]["message"]["content"]
+    
+    except RequestException as e:
+        logger.error(f"LLM request failed: {e}")
+        raise RuntimeError(f"Failed to connect to LLM at {OPENAPI_URL}: {str(e)}")
+    except Exception as e:
+        logger.error(f"LLM call failed: {e}")
+        raise RuntimeError(f"LLM call failed: {str(e)}")
 
 
-def chunk_text(text: str) -> list:
+def chunk_text(text: str) -> List[str]:
     """Split text into chunks with overlap for summarization."""
-    chunk_size = int(os.environ.get("CHUNK_SIZE", "4000"))
-    overlap = int(os.environ.get("OVERLAP", "200"))
-    
-    if len(text) <= chunk_size:
+    if len(text) <= CHUNK_SIZE:
         return [text]
     
     chunks = []
     start = 0
     
     while start < len(text):
-        end = min(start + chunk_size, len(text))
+        end = min(start + CHUNK_SIZE, len(text))
         
-        # Try to break at sentence/paragraph boundary
         break_point = end
         for marker in ["\n\n", "\n", ". ", "! ", "? "]:
-            pos = text.rfind(marker, start + chunk_size // 2, end)
+            pos = text.rfind(marker, start + CHUNK_SIZE // 2, end)
             if pos > start:
                 break_point = pos
                 break
@@ -132,84 +149,46 @@ def chunk_text(text: str) -> list:
         if chunk.strip():
             chunks.append(chunk)
         
-        start = break_point - overlap if break_point < len(text) else len(text)
+        start = break_point - OVERLAP if break_point < len(text) else len(text)
         if start >= len(text):
             break
     
+    logger.info(f"Split text into {len(chunks)} chunks")
     return chunks
 
 
-def summarize_document(text: str, max_length: int = 100) -> dict:
-    """
-    Main summarization function.
+def summarize_chunk(chunk_text: str, chunk_num: int, total_chunks: int) -> str:
+    """Summarize a single chunk of text."""
+    system_prompt = f"""You are a precise legal assistant creating concise, accurate summaries.
 
-    - If text is short, summarize directly
-    - If text is long, chunk and summarize each chunk, then synthesize
-    """
-    original_length = len(text)
-    
-    text = text.strip()
-    if not text:
-        raise ValueError("Empty text provided")
-    
-    max_direct_length = int(os.environ.get("MAX_DIRECT_TEXT_LENGTH", "8000"))
-    intermediate_length = int(os.environ.get("TARGET_INTERMEDIATE_SUMMARY_LENGTH", "150"))
-    
-    # Direct summarization for shorter texts
-    if len(text) <= max_direct_length:
-        system_prompt = f"""You are a precise legal assistant creating concise, accurate summaries.
-
-Create a summary that:
-- Is approximately {max_length} words
-- Captures key points and important details
-- Uses clear, professional language
-- Preserves names, dates, and specific facts
-
-Format as plain text without bullet points."""
-        
-        user_prompt = f"""Summarize the following document:
-
-{text}
-
-Summary:"""
-        
-        summary = call_llm(user_prompt, system_prompt)
-        
-        return {
-            "summary": summary,
-            "original_length": original_length,
-            "method": "direct",
-            "chunks": 1
-        }
-    
-    # Chunked summarization for longer texts
-    chunks = chunk_text(text)
-    
-    chunk_summaries = []
-    for i, chunk in enumerate(chunks, 1):
-        system_prompt = f"""You are a precise legal assistant creating concise, accurate summaries.
-
-You are processing chunk {i} of {len(chunks)} from a larger document.
+You are processing chunk {chunk_num} of {total_chunks} from a larger document.
 
 Create a focused summary that:
 - Captures key points and important details
-- Is approximately {intermediate_length} words
+- Is approximately {TARGET_INTERMEDIATE_SUMMARY_LENGTH} words
 - Can be combined with other chunk summaries
 - Uses clear, professional language
 - Preserves names, dates, and specific facts
 
 Respond as plain text without bullet points."""
     
-        user_prompt = f"""Summarize this text (chunk {i} of {len(chunks)}):
+    user_prompt = f"""Summarize this text (chunk {chunk_num} of {total_chunks}):
 
-{chunk}
+{chunk_text}
 
 Summary:"""
     
-        chunk_summary = call_llm(user_prompt, system_prompt)
-        chunk_summaries.append(chunk_summary)
+    messages = [
+        {"role": "system", "content": system_prompt},
+        {"role": "user", "content": user_prompt}
+    ]
     
-    # Synthesize into final summary
+    logger.info(f"Summarizing chunk {chunk_num}/{total_chunks}")
+    return call_llm(messages)
+
+
+def synthesize_summaries(chunk_summaries: List[str]) -> str:
+    """Synthesize multiple chunk summaries into a single final summary."""
     combined = "\n\n".join(chunk_summaries)
     
     system_prompt = """You are a precise legal assistant creating executive-level summaries.
@@ -230,7 +209,71 @@ Format as a single paragraph of plain text."""
 
 Final summary:"""
     
-    final_summary = call_llm(user_prompt, system_prompt)
+    messages = [
+        {"role": "system", "content": system_prompt},
+        {"role": "user", "content": user_prompt}
+    ]
+    
+    logger.info(f"Synthesizing {len(chunk_summaries)} chunk summaries")
+    return call_llm(messages)
+
+
+def summarize_document(text: str, max_length: int = MAX_DIRECT_SUMMARY_LENGTH) -> Dict[str, Any]:
+    """
+    Main summarization function.
+    
+    - If text is short, summarize directly
+    - If text is long, chunk and summarize each chunk, then synthesize
+    """
+    original_length = len(text)
+    
+    text = text.strip()
+    if not text:
+        raise ValueError("Empty text provided")
+    
+    logger.info(f"Summarizing text of {original_length} characters")
+    
+    # Direct summarization for shorter texts
+    if len(text) <= MAX_DIRECT_TEXT_LENGTH:
+        system_prompt = f"""You are a precise legal assistant creating concise, accurate summaries.
+
+Create a summary that:
+- Is approximately {max_length} words
+- Captures key points and important details
+- Uses clear, professional language
+- Preserves names, dates, and specific facts
+
+Format as plain text without bullet points."""
+        
+        user_prompt = f"""Summarize the following document:
+
+{text}
+
+Summary:"""
+        
+        messages = [
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": user_prompt}
+        ]
+        
+        summary = call_llm(messages)
+        
+        return {
+            "summary": summary,
+            "original_length": original_length,
+            "method": "direct",
+            "chunks": 1
+        }
+    
+    # Chunked summarization for longer texts
+    chunks = chunk_text(text)
+    
+    chunk_summaries = []
+    for i, chunk in enumerate(chunks, 1):
+        chunk_summary = summarize_chunk(chunk, i, len(chunks))
+        chunk_summaries.append(chunk_summary)
+    
+    final_summary = synthesize_summaries(chunk_summaries)
     
     return {
         "summary": final_summary,
@@ -244,8 +287,7 @@ class MCPSummaryHandler(BaseHTTPRequestHandler):
     """HTTP handler for MCP summary server."""
     
     def log_message(self, format, *args):
-        # Quiet logs by default
-        pass
+        logger.info(format % args)
     
     def _send_json(self, status: int, payload: Any):
         """Send JSON response."""
@@ -256,8 +298,8 @@ class MCPSummaryHandler(BaseHTTPRequestHandler):
         self.end_headers()
         self.wfile.write(body)
     
-    def _auth_or_401(self) -> bool:
-        """Check authentication if API key is configured."""
+    def _auth_or_401(self):
+        """Check authentication. Returns False if auth fails."""
         try:
             return require_auth(self.headers)
         except PermissionError:
@@ -270,6 +312,8 @@ class MCPSummaryHandler(BaseHTTPRequestHandler):
             self._send_json(200, {
                 "service": "mcp-summary",
                 "transport": "streamable-http",
+                "model": MODEL_NAME,
+                "status": "running",
                 "docs": "Use POST / with MCP JSON-RPC (initialize, tools/list, tools/call)."
             })
             return
@@ -278,6 +322,7 @@ class MCPSummaryHandler(BaseHTTPRequestHandler):
     
     def do_POST(self):
         """Handle MCP JSON-RPC requests."""
+        # Streamable HTTP MCP endpoint
         if self.path not in ("/", "/mcp"):
             self.send_error(404, "Not Found")
             return
@@ -301,6 +346,8 @@ class MCPSummaryHandler(BaseHTTPRequestHandler):
         params = req.get("params") or {}
         req_id = req.get("id")
         
+        logger.info(f"MCP request: method={method}, id={req_id}")
+        
         # MCP: initialize
         if method == "initialize":
             self._send_json(200, {
@@ -319,6 +366,15 @@ class MCPSummaryHandler(BaseHTTPRequestHandler):
             })
             return
         
+        # MCP: ping
+        if method == "ping":
+            self._send_json(200, {
+                "jsonrpc": "2.0",
+                "id": req_id,
+                "result": {}
+            })
+            return
+        
         # MCP: tools/list
         if method == "tools/list":
             self._send_json(200, {
@@ -344,6 +400,7 @@ class MCPSummaryHandler(BaseHTTPRequestHandler):
                     }
                 })
             except Exception as e:
+                logger.error(f"Tool call failed: {e}", exc_info=True)
                 self._send_json(200, {
                     "jsonrpc": "2.0",
                     "id": req_id,
@@ -364,7 +421,7 @@ class MCPSummaryHandler(BaseHTTPRequestHandler):
             if not text:
                 raise ValueError("Text parameter is required")
             
-            max_length = args.get("max_length", 100)
+            max_length = args.get("max_length", MAX_DIRECT_SUMMARY_LENGTH)
             return summarize_document(text, max_length)
         
         raise ValueError(f"Unknown tool: {name}")
@@ -376,6 +433,11 @@ def main():
     server = HTTPServer(("0.0.0.0", port), MCPSummaryHandler)
     mode = "auth enabled (Bearer)" if API_KEY else "no auth (API_KEY not set)"
     print(f"MCP Summary Server listening on 0.0.0.0:{port} [{mode}]")
+    print(f"  - Model: {MODEL_NAME}")
+    print(f"  - LLM URL: {OPENAPI_URL}")
+    print(f"  - Chunk size: {CHUNK_SIZE} characters")
+    print(f"  - Max direct text: {MAX_DIRECT_TEXT_LENGTH} characters")
+    print(f"  - LLM timeout: {LLM_TIMEOUT} seconds")
     try:
         server.serve_forever()
     except KeyboardInterrupt:

requirements.txt

@@ -1,4 +0,0 @@
-# requirements.txt for MCP Summary Server
-
-# HTTP requests for LLM communication
-requests>=2.31.0