380 lines
11 KiB
Python
380 lines
11 KiB
Python
#!/usr/bin/env python3
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"""
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MCP Summary Server (Streamable HTTP transport)
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Designed to work with OpenWebUI's MCP (Streamable HTTP) integration.
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Summarizes documents by:
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1. Checking text length
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2. If short, summarizing directly with LLM
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3. If long, chunking text, summarizing each chunk, then synthesizing
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All processing happens server-side, keeping full text out of the chat context window.
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Tools:
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- summarize_document: Summarize a document (handles chunking automatically)
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Auth:
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- If API_KEY is set:
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- Requires: Authorization: Bearer <API_KEY>
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- If API_KEY is not set:
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- No auth required (for local/internal use).
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"""
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import json
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import os
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import sys
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import re
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from http.server import HTTPServer, BaseHTTPRequestHandler
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from typing import Any, Dict, Optional
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import requests
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API_KEY = os.environ.get("API_KEY", "").strip()
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TOOLS_LIST: Dict[str, Any] = {
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"tools": [
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{
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"name": "summarize_document",
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"description": "Summarize a document. Automatically handles chunking for long text. Returns a concise summary without exposing the full text.",
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"inputSchema": {
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"type": "object",
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"properties": {
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"text": {
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"type": "string",
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"description": "The document text to summarize"
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},
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"max_length": {
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"type": "integer",
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"description": "Maximum length of summary in words (default: 100)"
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}
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},
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"required": ["text"]
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}
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}
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]
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}
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def get_bearer_token(headers: Any) -> Optional[str]:
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auth = (headers.get("Authorization") or "").strip()
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if auth.startswith("Bearer "):
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return auth[len("Bearer "):].strip()
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return None
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def require_auth(headers: Any) -> bool:
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# If API_KEY is not set, allow unauthenticated access
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if not API_KEY:
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return True
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token = get_bearer_token(headers)
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if not token or token != API_KEY:
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raise PermissionError("Missing or invalid API key")
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return True
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def call_llm(text: str, system_prompt: str, max_tokens: int = 2000) -> str:
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"""Make an OpenAPI-compatible LLM call."""
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openapi_url = os.environ.get("OPENAPI_URL", "http://localhost:8080/v1")
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openapi_api_key = os.environ.get("OPENAPI_API_KEY", "")
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model_name = os.environ.get("MODEL_NAME", "gpt-4o")
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timeout = int(os.environ.get("LLM_TIMEOUT", "120"))
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url = f"{openapi_url}/chat/completions"
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headers = {
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"Content-Type": "application/json",
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"Authorization": f"Bearer {openapi_api_key}"
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}
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payload = {
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"model": model_name,
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"messages": [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": text}
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],
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"temperature": 0.3,
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"max_tokens": max_tokens,
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"top_p": 0.9
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}
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response = requests.post(url, headers=headers, json=payload, timeout=timeout)
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response.raise_for_status()
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data = response.json()
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return data["choices"][0]["message"]["content"]
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def chunk_text(text: str) -> list:
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"""Split text into chunks with overlap for summarization."""
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chunk_size = int(os.environ.get("CHUNK_SIZE", "4000"))
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overlap = int(os.environ.get("OVERLAP", "200"))
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if len(text) <= chunk_size:
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return [text]
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chunks = []
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start = 0
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while start < len(text):
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end = min(start + chunk_size, len(text))
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# Try to break at sentence/paragraph boundary
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break_point = end
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for marker in ["\n\n", "\n", ". ", "! ", "? "]:
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pos = text.rfind(marker, start + chunk_size // 2, end)
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if pos > start:
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break_point = pos
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break
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chunk = text[start:break_point]
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if chunk.strip():
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chunks.append(chunk)
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start = break_point - overlap if break_point < len(text) else len(text)
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if start >= len(text):
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break
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return chunks
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def summarize_document(text: str, max_length: int = 100) -> dict:
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"""
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Main summarization function.
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- If text is short, summarize directly
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- If text is long, chunk and summarize each chunk, then synthesize
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"""
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original_length = len(text)
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text = text.strip()
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if not text:
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raise ValueError("Empty text provided")
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max_direct_length = int(os.environ.get("MAX_DIRECT_TEXT_LENGTH", "8000"))
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intermediate_length = int(os.environ.get("TARGET_INTERMEDIATE_SUMMARY_LENGTH", "150"))
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# Direct summarization for shorter texts
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if len(text) <= max_direct_length:
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system_prompt = f"""You are a precise legal assistant creating concise, accurate summaries.
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Create a summary that:
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- Is approximately {max_length} words
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- Captures key points and important details
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- Uses clear, professional language
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- Preserves names, dates, and specific facts
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Format as plain text without bullet points."""
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user_prompt = f"""Summarize the following document:
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{text}
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Summary:"""
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summary = call_llm(user_prompt, system_prompt)
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return {
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"summary": summary,
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"original_length": original_length,
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"method": "direct",
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"chunks": 1
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}
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# Chunked summarization for longer texts
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chunks = chunk_text(text)
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chunk_summaries = []
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for i, chunk in enumerate(chunks, 1):
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system_prompt = f"""You are a precise legal assistant creating concise, accurate summaries.
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You are processing chunk {i} of {len(chunks)} from a larger document.
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Create a focused summary that:
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- Captures key points and important details
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- Is approximately {intermediate_length} words
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- Can be combined with other chunk summaries
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- Uses clear, professional language
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- Preserves names, dates, and specific facts
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Respond as plain text without bullet points."""
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user_prompt = f"""Summarize this text (chunk {i} of {len(chunks)}):
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{chunk}
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Summary:"""
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chunk_summary = call_llm(user_prompt, system_prompt)
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chunk_summaries.append(chunk_summary)
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# Synthesize into final summary
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combined = "\n\n".join(chunk_summaries)
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system_prompt = """You are a precise legal assistant creating executive-level summaries.
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Synthesize the provided partial summaries into a single, cohesive summary that:
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- Is approximately 100 words
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- Captures the complete document picture
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- Is clear and professional
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- Removes redundancy
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- Maintains logical flow
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- Preserves all critical information
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Format as a single paragraph of plain text."""
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user_prompt = f"""Synthesize these partial summaries into one cohesive summary:
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{combined}
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Final summary:"""
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final_summary = call_llm(user_prompt, system_prompt)
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return {
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"summary": final_summary,
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"original_length": original_length,
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"method": "chunked",
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"chunks": len(chunks)
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}
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class MCPSummaryHandler(BaseHTTPRequestHandler):
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def log_message(self, format, *args):
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# Quiet logs by default
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pass
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def _send_json(self, status: int, payload: Any):
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body = json.dumps(payload, ensure_ascii=False).encode("utf-8")
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self.send_response(status)
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self.send_header("Content-Type", "application/json")
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self.send_header("Content-Length", str(len(body)))
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self.end_headers()
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self.wfile.write(body)
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def _auth_or_401(self):
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try:
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return require_auth(self.headers)
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except PermissionError:
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self._send_json(401, {"error": "Missing or invalid API key"})
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return False
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def do_GET(self):
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# Basic info endpoint (not required by MCP, but useful)
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if self.path == "/":
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self._send_json(200, {
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"service": "mcp-summary",
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"transport": "streamable-http",
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"docs": "Use POST / with MCP JSON-RPC (initialize, tools/list, tools/call)."
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})
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return
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self.send_error(404, "Not Found")
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def do_POST(self):
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# Streamable HTTP MCP endpoint
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if self.path not in ("/", "/mcp"):
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self.send_error(404, "Not Found")
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return
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if not self._auth_or_401():
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return
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length = int(self.headers.get("Content-Length", 0))
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if length == 0:
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self._send_json(400, {"error": "Empty body"})
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return
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raw = self.rfile.read(length)
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try:
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req = json.loads(raw)
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except json.JSONDecodeError:
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self._send_json(400, {"error": "Invalid JSON"})
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return
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method = req.get("method")
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params = req.get("params") or {}
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req_id = req.get("id")
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# MCP: initialize
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if method == "initialize":
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self._send_json(200, {
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"jsonrpc": "2.0",
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"id": req_id,
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"result": {
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"protocolVersion": "2025-11-25",
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"capabilities": {
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"tools": {}
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},
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"serverInfo": {
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"name": "mcp-summary",
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"version": "1.0.0"
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}
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}
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})
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return
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# MCP: tools/list
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if method == "tools/list":
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self._send_json(200, {
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"jsonrpc": "2.0",
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"id": req_id,
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"result": TOOLS_LIST
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})
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return
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# MCP: tools/call
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if method == "tools/call":
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tool_name = params.get("name")
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tool_args = params.get("arguments") or {}
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try:
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result = self._call_tool(tool_name, tool_args)
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self._send_json(200, {
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"jsonrpc": "2.0",
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"id": req_id,
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"result": {
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"content": [
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{"type": "text", "text": json.dumps(result, ensure_ascii=False)}
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]
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}
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})
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except Exception as e:
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self._send_json(200, {
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"jsonrpc": "2.0",
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"id": req_id,
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"error": {
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"code": -32000,
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"message": str(e)
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}
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})
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return
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# Unknown method
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self._send_json(400, {"error": "Unknown method: " + str(method)})
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def _call_tool(self, name: str, args: Dict[str, Any]) -> Any:
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if name == "summarize_document":
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text = args.get("text")
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if not text:
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raise ValueError("Text parameter is required")
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max_length = args.get("max_length", 100)
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return summarize_document(text, max_length)
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raise ValueError(f"Unknown tool: {name}")
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def main():
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port = int(sys.argv[1]) if len(sys.argv) > 1 else int(os.environ.get("PORT", "8080"))
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server = HTTPServer(("0.0.0.0", port), MCPSummaryHandler)
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mode = "auth enabled (Bearer)" if API_KEY else "no auth (API_KEY not set)"
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print(f"MCP Summary Server listening on 0.0.0.0:{port} [{mode}]")
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try:
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server.serve_forever()
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except KeyboardInterrupt:
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print("\nShutting down...")
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server.server_close()
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if __name__ == "__main__":
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main()
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