Initial commit: MCP Summary Server

.env.example

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+# 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
+
+# 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

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+# 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 API_KEY=""
+
+# Start the MCP summary server
+ENTRYPOINT ["python", "-u", "/app/mcp_summary_server.py"]

README.md

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+# 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 |
+| 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
+```
+
+## 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
+}
+```

mcp_summary_server.py

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+#!/usr/bin/env python3
+"""
+MCP Summary Server (Streamable HTTP transport)
+
+Designed to work with OpenWebUI's MCP (Streamable HTTP) integration.
+
+Summarizes documents by:
+1. Checking text length
+2. If short, summarizing directly with LLM
+3. If long, chunking text, summarizing each chunk, then synthesizing
+
+All processing happens server-side, keeping full text out of the chat context window.
+
+Tools:
+- summarize_document: Summarize a document (handles chunking automatically)
+
+Auth:
+- If API_KEY is set:
+  - Requires: Authorization: Bearer <API_KEY>
+- If API_KEY is not set:
+  - No auth required (for local/internal use).
+"""
+
+import json
+import os
+import sys
+import re
+from http.server import HTTPServer, BaseHTTPRequestHandler
+from typing import Any, Dict, List, Optional
+import requests
+
+# 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"))  # Characters per chunk
+OVERLAP = int(os.environ.get("OVERLAP", "200"))  # Characters of overlap between chunks
+TARGET_INTERMEDIATE_SUMMARY_LENGTH = int(os.environ.get("TARGET_INTERMEDIATE_SUMMARY_LENGTH", "150"))  # Words
+MAX_DIRECT_SUMMARY_LENGTH = int(os.environ.get("MAX_DIRECT_SUMMARY_LENGTH", "100"))  # Words for final summary
+MAX_DIRECT_TEXT_LENGTH = int(os.environ.get("MAX_DIRECT_TEXT_LENGTH", "8000"))  # Characters before chunking
+
+# Tool definitions
+TOOLS_LIST: Dict[str, Any] = {
+    "tools": [
+        {
+            "name": "summarize_document",
+            "description": "Summarize a document. Automatically handles chunking for long text. Returns a concise summary without exposing the full text.",
+            "inputSchema": {
+                "type": "object",
+                "properties": {
+                    "text": {
+                        "type": "string",
+                        "description": "The document text to summarize"
+                    },
+                    "max_length": {
+                        "type": "integer",
+                        "description": f"Maximum length of summary in words (default: {MAX_DIRECT_SUMMARY_LENGTH})"
+                    }
+                },
+                "required": ["text"]
+            }
+        }
+    ]
+}
+
+
+def call_llm(messages: List[Dict], temperature: float = 0.3) -> str:
+    """Make an OpenAPI-compatible LLM call."""
+    url = f"{OPENAPI_URL}/chat/completions"
+    headers = {
+        "Content-Type": "application/json",
+        "Authorization": f"Bearer {OPENAPI_API_KEY}"
+    }
+    
+    payload = {
+        "model": MODEL_NAME,
+        "messages": messages,
+        "temperature": temperature,
+        "max_tokens": 2000,
+        "top_p": 0.9
+    }
+    
+    response = requests.post(url, headers=headers, json=payload, timeout=60)
+    response.raise_for_status()
+    
+    data = response.json()
+    return data["choices"][0]["message"]["content"]
+
+
+def chunk_text(text: str) -> List[str]:
+    """Split text into chunks with overlap for summarization."""
+    if len(text) <= CHUNK_SIZE:
+        return [text]
+    
+    chunks = []
+    start = 0
+    
+    while start < len(text):
+        # Find a good breaking point (after sentence or paragraph)
+        end = min(start + CHUNK_SIZE, len(text))
+        
+        # Try to break at sentence boundary
+        search_end = min(end, len(text))
+        break_point = -1
+        
+        # Look for paragraph break first
+        for marker in ["\n\n", "\n"]:
+            pos = text.rfind(marker, start + CHUNK_SIZE // 2, search_end)
+            if pos > 0:
+                break_point = pos
+                break
+        
+        # If no paragraph break, look for sentence break
+        if break_point == -1:
+            for marker in [".", "!", "?"]:
+                pos = text.rfind(marker, start + CHUNK_SIZE // 2, search_end)
+                if pos > 0:
+                    break_point = pos
+                    break
+        
+        if break_point == -1:
+            break_point = end
+        
+        chunk = text[start:break_point]
+        if chunk.strip():
+            chunks.append(chunk)
+        
+        start = break_point - OVERLAP if break_point < len(text) else len(text)
+        if start >= len(text):
+            break
+    
+    return chunks
+
+
+def summarize_chunk(chunk: str, chunk_num: int, total_chunks: int) -> str:
+    """Summarize a single chunk of text."""
+    system_prompt = f"""You are a precise legal assistant specializing in creating concise, accurate summaries.
+
+You are processing chunk {chunk_num} of {total_chunks} from a larger document.
+
+Your task: Create a focused summary of this chunk that:
+- Captures the key points and important details
+- Is approximately {TARGET_INTERMEDIATE_SUMMARY_LENGTH} words
+- Can be combined with summaries of other chunks to form a complete picture
+- Uses clear, professional language
+- Preserves important names, dates, and specific facts
+
+Format your response as plain text without bullet points or special formatting."""
+    
+    user_prompt = f"""Summarize the following text (chunk {chunk_num} of {total_chunks}):
+
+{text}
+
+Summary:"""
+    
+    messages = [
+        {"role": "system", "content": system_prompt},
+        {"role": "user", "content": user_prompt}
+    ]
+    
+    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.
+
+Your task: Synthesize the provided partial summaries into a single, cohesive summary that:
+- Is approximately 100 words
+- Captures the complete picture of the document
+- Is clear and professional
+- Removes redundancy
+- Maintains logical flow
+- Preserves all critical information
+
+Format your response as a single paragraph of plain text."""
+    
+    user_prompt = f"""Synthesize the following partial summaries into one cohesive summary:
+
+{combined}
+
+Final summary:"""
+    
+    messages = [
+        {"role": "system", "content": system_prompt},
+        {"role": "user", "content": user_prompt}
+    ]
+    
+    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)
+    
+    # Strip whitespace and validate
+    text = text.strip()
+    if not text:
+        raise ValueError("Empty text provided")
+    
+    # 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.
+
+Your task: Create a summary that:
+- Is approximately {max_length} words
+- Captures the key points and important details
+- Uses clear, professional language
+- Preserves important names, dates, and specific facts
+- Is suitable for a legal professional
+
+Format your response as plain text without bullet points or special formatting."""
+        
+        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)
+    
+    # Summarize each chunk
+    chunk_summaries = []
+    for i, chunk in enumerate(chunks, 1):
+        chunk_summary = summarize_chunk(chunk, i, len(chunks))
+        chunk_summaries.append(chunk_summary)
+    
+    # Synthesize into final summary
+    final_summary = synthesize_summaries(chunk_summaries)
+    
+    return {
+        "summary": final_summary,
+        "original_length": original_length,
+        "method": "chunked",
+        "chunks": len(chunks)
+    }
+
+
+class MCPSummaryHandler(BaseHTTPRequestHandler):
+    """HTTP handler for MCP summary server."""
+    
+    def log_message(self, format, *args):
+        # Quiet logs by default
+        pass
+    
+    def _send_json(self, status: int, payload: Any):
+        """Send JSON response."""
+        body = json.dumps(payload, ensure_ascii=False).encode("utf-8")
+        self.send_response(status)
+        self.send_header("Content-Type", "application/json")
+        self.send_header("Content-Length", str(len(body)))
+        self.end_headers()
+        self.wfile.write(body)
+    
+    def _auth_or_401(self) -> bool:
+        """Check authentication if API key is configured."""
+        if not API_KEY:
+            return True
+        
+        auth_header = self.headers.get("Authorization", "")
+        if not auth_header.startswith("Bearer "):
+            self._send_json(401, {"error": "Missing or invalid API key"})
+            return False
+        
+        token = auth_header[len("Bearer "):].strip()
+        if token != API_KEY:
+            self._send_json(401, {"error": "Invalid API key"})
+            return False
+        
+        return True
+    
+    def do_GET(self):
+        """Handle GET requests (health check)."""
+        if self.path == "/":
+            self._send_json(200, {
+                "service": "mcp-summary",
+                "transport": "streamable-http",
+                "model": MODEL_NAME,
+                "docs": "Use POST / with MCP JSON-RPC (initialize, tools/list, tools/call)."
+            })
+            return
+        
+        self.send_error(404, "Not Found")
+    
+    def do_POST(self):
+        """Handle MCP JSON-RPC requests."""
+        if self.path not in ("/", "/mcp"):
+            self.send_error(404, "Not Found")
+            return
+        
+        if not self._auth_or_401():
+            return
+        
+        # Parse request
+        length = int(self.headers.get("Content-Length", 0))
+        if length == 0:
+            self._send_json(400, {"error": "Empty body"})
+            return
+        
+        raw = self.rfile.read(length)
+        try:
+            req = json.loads(raw)
+        except json.JSONDecodeError:
+            self._send_json(400, {"error": "Invalid JSON"})
+            return
+        
+        method = req.get("method")
+        params = req.get("params") or {}
+        req_id = req.get("id")
+        
+        # MCP: initialize
+        if method == "initialize":
+            self._send_json(200, {
+                "jsonrpc": "2.0",
+                "id": req_id,
+                "result": {
+                    "protocolVersion": "2025-11-25",
+                    "capabilities": {
+                        "tools": {}
+                    },
+                    "serverInfo": {
+                        "name": "mcp-summary",
+                        "version": "1.0.0"
+                    }
+                }
+            })
+            return
+        
+        # MCP: tools/list
+        if method == "tools/list":
+            self._send_json(200, {
+                "jsonrpc": "2.0",
+                "id": req_id,
+                "result": TOOLS_LIST
+            })
+            return
+        
+        # MCP: tools/call
+        if method == "tools/call":
+            tool_name = params.get("name")
+            tool_args = params.get("arguments") or {}
+            
+            try:
+                result = self._call_tool(tool_name, tool_args)
+                self._send_json(200, {
+                    "jsonrpc": "2.0",
+                    "id": req_id,
+                    "result": {
+                        "content": [
+                            {"type": "text", "text": json.dumps(result, ensure_ascii=False)}
+                        ]
+                    }
+                })
+            except Exception as e:
+                self._send_json(200, {
+                    "jsonrpc": "2.0",
+                    "id": req_id,
+                    "error": {
+                        "code": -32000,
+                        "message": str(e)
+                    }
+                })
+            return
+        
+        # Unknown method
+        self._send_json(400, {"error": "Unknown method: " + str(method)})
+    
+    def _call_tool(self, name: str, args: Dict[str, Any]) -> Any:
+        """Execute a tool call."""
+        if name == "summarize_document":
+            text = args.get("text")
+            if not text:
+                raise ValueError("Text parameter is required")
+            
+            max_length = args.get("max_length", MAX_DIRECT_SUMMARY_LENGTH)
+            return summarize_document(text, max_length)
+        
+        raise ValueError(f"Unknown tool: {name}")
+
+
+def main():
+    """Start the MCP summary server."""
+    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"  - Chunk size: {CHUNK_SIZE} characters")
+    print(f"  - Max direct text: {MAX_DIRECT_TEXT_LENGTH} characters")
+    
+    try:
+        server.serve_forever()
+    except KeyboardInterrupt:
+        print("\nShutting down...")
+        server.server_close()
+
+
+if __name__ == "__main__":
+    main()

requirements.txt

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+# requirements.txt for MCP Summary Server
+
+# HTTP requests for LLM communication
+requests>=2.31.0