713 lines
26 KiB
Python
713 lines
26 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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Features:
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- Multiple specialized summarization, comparison, and extraction tools.
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- Automatic chunking and synthesis for long documents.
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- Temporary in-memory storage of document chunks/summaries for continued use.
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- Configurable cache limits via environment variables.
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Tools:
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- summarize_document
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- summarize_executive_brief
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- summarize_bullet_points
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- summarize_for_court
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- compare_documents
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- extract_key_points
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- extract_action_items
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- extract_entities
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- summarize_very_long_document
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- retrieve_document_data
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- query_stored_document
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- clear_document_cache
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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 time
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import uuid
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import logging
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from http.server import HTTPServer, BaseHTTPRequestHandler
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from typing import Any, Dict, List, Optional, Tuple
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import requests
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# Configure logging
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logging.basicConfig(
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level=logging.INFO,
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format="%(asctime)s [%(levelname)s] %(message)s",
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stream=sys.stdout,
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)
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logger = logging.getLogger("mcp-summary")
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# MCP Server Configuration
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API_KEY = os.environ.get("API_KEY", "").strip()
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PORT = int(os.environ.get("PORT", "8080"))
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# LLM Configuration
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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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LLM_TIMEOUT = int(os.environ.get("LLM_TIMEOUT", "120"))
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# Chunking Configuration
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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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MAX_DIRECT_TEXT_LENGTH = int(os.environ.get("MAX_DIRECT_TEXT_LENGTH", "8000"))
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TARGET_INTERMEDIATE_SUMMARY_LENGTH = int(os.environ.get("TARGET_INTERMEDIATE_SUMMARY_LENGTH", "150"))
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# Cache Configuration
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MAX_STORED_DOCS = int(os.environ.get("MAX_STORED_DOCS", "500"))
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CACHE_TTL_SECONDS = int(os.environ.get("CACHE_TTL_SECONDS", "86400")) # 24h default
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# Temporary in-memory store
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DOCUMENT_STORE: Dict[str, Dict[str, Any]] = {}
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def generate_doc_id() -> str:
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return str(uuid.uuid4())
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def evict_oldest_if_needed():
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if len(DOCUMENT_STORE) <= MAX_STORED_DOCS:
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return
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# Remove oldest N entries to stay within limit
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sorted_keys = sorted(DOCUMENT_STORE.keys(), key=lambda k: DOCUMENT_STORE[k]["created_at"])
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to_remove = len(DOCUMENT_STORE) - MAX_STORED_DOCS
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for k in sorted_keys[:to_remove]:
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DOCUMENT_STORE.pop(k, None)
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def store_document(doc_id: str, text_length: int, chunks: List[str],
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intermediate_summaries: List[str], final_output: str,
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tool_used: str):
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evict_oldest_if_needed()
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DOCUMENT_STORE[doc_id] = {
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"text_length": text_length,
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"chunks_count": len(chunks),
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"chunks": chunks,
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"intermediate_summaries": intermediate_summaries,
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"final_output": final_output,
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"tool_used": tool_used,
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"created_at": time.time()
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}
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def get_document(doc_id: str) -> Optional[Dict[str, Any]]:
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doc = DOCUMENT_STORE.get(doc_id)
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if not doc:
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return None
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# TTL check
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if time.time() - doc["created_at"] > CACHE_TTL_SECONDS:
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DOCUMENT_STORE.pop(doc_id, None)
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return None
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return doc
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def call_llm(system_prompt: str, user_prompt: str, max_tokens: int = 2000) -> str:
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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": user_prompt}
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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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logger.info(f"Calling LLM: {OPENAPI_URL} model={MODEL_NAME}")
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response = requests.post(url, headers=headers, json=payload, timeout=LLM_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[str]:
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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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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 build_tool_prompts(tool_name: str) -> Tuple[str, str, str]:
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"""
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Returns (system_prompt, chunk_user_template, synthesis_user_template)
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Templates use {text} or {summaries} placeholders.
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"""
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base_system = "You are a precise legal assistant creating concise, accurate outputs."
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if tool_name == "summarize_document":
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sys_prompt = base_system + """
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Create a clear, professional summary.
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- Approximately {max_length} words.
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- Capture key points, important details, names, dates, facts.
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- Format as plain text without bullet points.
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"""
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chunk_user = "Summarize this text (chunk {i} of {total}):\n\n{text}\n\nSummary:"
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synth_user = "Synthesize these partial summaries into one cohesive summary:\n\n{summaries}\n\nFinal summary:"
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elif tool_name == "summarize_executive_brief":
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sys_prompt = base_system + """
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Create an executive brief:
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- 1–2 paragraphs.
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- High-level overview of issues, key findings, and outcomes.
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- Professional tone, suitable for senior decision-makers.
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- No bullet points.
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"""
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chunk_user = "Provide a concise executive-style summary of this chunk (chunk {i} of {total}):\n\n{text}\n\nExecutive summary:"
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synth_user = "Combine these executive-style summaries into a single, clear executive brief:\n\n{summaries}\n\nFinal executive brief:"
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elif tool_name == "summarize_bullet_points":
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sys_prompt = base_system + """
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Create a concise bullet-point summary:
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- Use short bullets.
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- Focus on key points, actions, dates, and outcomes.
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- No long paragraphs.
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"""
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chunk_user = "Summarize this chunk as concise bullet points (chunk {i} of {total}):\n\n{text}\n\nBullet points:"
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synth_user = "Merge these bullet-point summaries into one clean, non-redundant bullet list:\n\n{summaries}\n\nFinal bullet summary:"
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elif tool_name == "summarize_for_court":
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sys_prompt = base_system + """
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Create a summary suitable for a judge or legal professional:
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- Clearly state: parties, issues, key evidence, legal reasoning, outcome.
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- Use formal, precise language.
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- Keep it concise and structured.
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"""
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chunk_user = "Provide a court-style summary of this chunk (chunk {i} of {total}):\n\n{text}\n\nCourt summary:"
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synth_user = "Combine these summaries into a single, structured summary suitable for a court:\n\n{summaries}\n\nFinal court-style summary:"
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elif tool_name == "compare_documents":
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sys_prompt = base_system + """
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Compare two documents and highlight:
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- Key differences and conflicts.
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- Changes in facts, reasoning, or outcomes.
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- Any new or removed conditions/requirements.
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Be precise and concise.
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"""
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# For compare, we process both texts together; chunking applies if combined is long.
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chunk_user = "Compare these excerpts and note key differences/conflicts (chunk {i} of {total}):\n\n{text}\n\nComparison:"
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synth_user = "Synthesize these partial comparisons into a single, clear comparison summary:\n\n{summaries}\n\nFinal comparison:"
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elif tool_name == "extract_key_points":
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sys_prompt = base_system + """
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Extract the key points from the text:
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- Issues, holdings, obligations, dates, parties, statutes.
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- Use concise bullet points.
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- Do not add commentary.
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"""
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chunk_user = "Extract the key points from this chunk (chunk {i} of {total}):\n\n{text}\n\nKey points:"
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synth_user = "Combine these extracted key points into one clean, non-redundant list:\n\n{summaries}\n\nFinal key points:"
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elif tool_name == "extract_action_items":
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sys_prompt = base_system + """
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Extract all action items, deadlines, and obligations:
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- Who must do what, by when.
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- Use concise bullets.
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- No extra commentary.
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"""
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chunk_user = "Extract action items from this chunk (chunk {i} of {total}):\n\n{text}\n\nAction items:"
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synth_user = "Combine these action items into one clear, non-redundant list:\n\n{summaries}\n\nFinal action items:"
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elif tool_name == "extract_entities":
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sys_prompt = base_system + """
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Extract important entities:
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- People, organizations, locations, dates, legal references, case names.
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- Use concise bullets, grouped by type.
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- No extra commentary.
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"""
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chunk_user = "Extract entities from this chunk (chunk {i} of {total}):\n\n{text}\n\nEntities:"
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synth_user = "Merge these entity lists into one clean, grouped list:\n\n{summaries}\n\nFinal entities:"
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elif tool_name == "summarize_very_long_document":
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sys_prompt = base_system + """
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Create a concise, structured summary optimized for very long documents:
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- Preserve core issues, reasoning, outcomes, and critical details.
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- Use clear paragraphs; avoid fluff.
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"""
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chunk_user = "Summarize this chunk from a very long document (chunk {i} of {total}):\n\n{text}\n\nSummary:"
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synth_user = "Synthesize these summaries into one concise, structured summary of the full document:\n\n{summaries}\n\nFinal summary:"
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else:
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# Fallback
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sys_prompt = base_system
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chunk_user = "Process this chunk (chunk {i} of {total}):\n\n{text}"
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synth_user = "Combine these results:\n\n{summaries}"
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return sys_prompt, chunk_user, synth_user
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def process_with_chunking(
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text: str,
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tool_name: str,
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max_length: int = 100
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) -> Tuple[str, List[str], List[str]]:
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"""
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Returns (final_output, chunks, intermediate_summaries)
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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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sys_prompt, chunk_user_tpl, synth_user_tpl = build_tool_prompts(tool_name)
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# If short, direct processing
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if len(text) <= MAX_DIRECT_TEXT_LENGTH:
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user_prompt = chunk_user_tpl.format(
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i=1, total=1, text=text, max_length=max_length
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)
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final_output = call_llm(sys_prompt, user_prompt)
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return final_output, [text], [final_output]
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# Chunked processing
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chunks = chunk_text(text)
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intermediate_summaries = []
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for i, chunk in enumerate(chunks, 1):
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user_prompt = chunk_user_tpl.format(i=i, total=len(chunks), text=chunk)
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summary = call_llm(sys_prompt, user_prompt)
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intermediate_summaries.append(summary)
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# Synthesis
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combined = "\n\n".join(intermediate_summaries)
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synth_prompt = synth_user_tpl.format(summaries=combined)
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final_output = call_llm(sys_prompt, synth_prompt)
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return final_output, chunks, intermediate_summaries
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def compare_texts_with_chunking(text1: str, text2: str) -> Tuple[str, List[str], List[str]]:
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combined = f"=== DOCUMENT 1 ===\n\n{text1}\n\n=== DOCUMENT 2 ===\n\n{text2}"
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return process_with_chunking(combined, "compare_documents")
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def query_chunks(chunks: List[str], question: str) -> str:
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"""
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Simple semantic-style query: send question + chunks to LLM to extract relevant answers.
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For very large chunk lists, we can limit or sample; here we send all but keep prompt tight.
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"""
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system_prompt = (
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"You are a precise legal assistant. Answer the question strictly based on the provided text. "
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"If the information is not present, say so clearly."
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)
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user_prompt = (
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"Question:\n"
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f"{question}\n\n"
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"Text:\n"
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+ "\n\n".join(chunks)
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)
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return call_llm(system_prompt, user_prompt, max_tokens=1500)
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# Tool definitions
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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": "General-purpose document summarization. Prefer this for long or complex documents to avoid context limits.",
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"inputSchema": {
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"type": "object",
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"properties": {
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"text": {"type": "string", "description": "Full document text to summarize."},
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"max_length": {"type": "integer", "description": "Max summary length in words (default: 100)."}
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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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"name": "summarize_executive_brief",
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"description": "Create a short executive brief (1–2 paragraphs) for senior decision-makers.",
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"inputSchema": {
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"type": "object",
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"properties": {
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"text": {"type": "string", "description": "Full document text."}
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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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"name": "summarize_bullet_points",
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"description": "Create a concise bullet-point summary of key points.",
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"inputSchema": {
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"type": "object",
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"properties": {
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"text": {"type": "string", "description": "Full document text."}
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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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"name": "summarize_for_court",
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"description": "Create a formal summary suitable for a judge or legal professional.",
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"inputSchema": {
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"type": "object",
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"properties": {
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"text": {"type": "string", "description": "Full document text."}
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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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"name": "compare_documents",
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"description": "Compare two documents and highlight key differences, conflicts, and changes.",
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"inputSchema": {
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"type": "object",
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"properties": {
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"text1": {"type": "string", "description": "First document text."},
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"text2": {"type": "string", "description": "Second document text."}
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},
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"required": ["text1", "text2"]
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}
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},
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{
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"name": "extract_key_points",
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"description": "Extract key points: issues, holdings, obligations, dates, parties, statutes.",
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"inputSchema": {
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"type": "object",
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"properties": {
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"text": {"type": "string", "description": "Full document text."}
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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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"name": "extract_action_items",
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"description": "Extract all action items, deadlines, and obligations.",
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"inputSchema": {
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"type": "object",
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"properties": {
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"text": {"type": "string", "description": "Full document text."}
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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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"name": "extract_entities",
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"description": "Extract important entities: people, organizations, locations, dates, legal references.",
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"inputSchema": {
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"type": "object",
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"properties": {
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"text": {"type": "string", "description": "Full document text."}
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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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"name": "summarize_very_long_document",
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"description": "Optimized for very long documents with deeper chunking and hierarchical summarization.",
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"inputSchema": {
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"type": "object",
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"properties": {
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"text": {"type": "string", "description": "Very long document text."}
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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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"name": "retrieve_document_data",
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"description": "Retrieve stored data for a previously processed document by doc_id (final output, intermediate summaries, metadata).",
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"inputSchema": {
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"type": "object",
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"properties": {
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"doc_id": {"type": "string", "description": "Document ID returned when the document was first processed."}
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},
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"required": ["doc_id"]
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}
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},
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{
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"name": "query_stored_document",
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"description": "Ask a question about a previously processed document using its stored chunks.",
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"inputSchema": {
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"type": "object",
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"properties": {
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"doc_id": {"type": "string", "description": "Document ID."},
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"question": {"type": "string", "description": "Your question about the document."}
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},
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"required": ["doc_id", "question"]
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}
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},
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{
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"name": "clear_document_cache",
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"description": "Clear all temporarily stored document data from this server.",
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"inputSchema": {
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"type": "object",
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"properties": {},
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"required": []
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}
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}
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]
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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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logger.info(format % args)
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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()
|
||
self.wfile.write(body)
|
||
|
||
def _auth_or_401(self) -> bool:
|
||
auth = (self.headers.get("Authorization") or "").strip()
|
||
if not API_KEY:
|
||
return True
|
||
if auth.startswith("Bearer "):
|
||
token = auth[len("Bearer "):].strip()
|
||
if token == API_KEY:
|
||
return True
|
||
self._send_json(401, {"error": "Missing or invalid API key"})
|
||
return False
|
||
|
||
def do_GET(self):
|
||
try:
|
||
if self.path == "/":
|
||
self._send_json(200, {
|
||
"service": "mcp-summary",
|
||
"transport": "streamable-http",
|
||
"docs": "Use POST / with MCP JSON-RPC (initialize, tools/list, tools/call)."
|
||
})
|
||
return
|
||
self.send_error(404, "Not Found")
|
||
except Exception as e:
|
||
logger.error(f"GET error: {e}", exc_info=True)
|
||
try:
|
||
self.send_error(500, "Internal Server Error")
|
||
except Exception:
|
||
pass
|
||
|
||
def do_POST(self):
|
||
try:
|
||
if self.path not in ("/", "/mcp"):
|
||
self.send_error(404, "Not Found")
|
||
return
|
||
|
||
if not self._auth_or_401():
|
||
return
|
||
|
||
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")
|
||
|
||
logger.info(f"MCP request: method={method}, id={req_id}")
|
||
|
||
# Notifications
|
||
if isinstance(method, str) and method.startswith("notifications/"):
|
||
if req_id is not None:
|
||
self._send_json(200, {"jsonrpc": "2.0", "id": req_id, "result": {}})
|
||
else:
|
||
self.send_response(200)
|
||
self.send_header("Content-Length", "0")
|
||
self.end_headers()
|
||
return
|
||
|
||
# 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
|
||
|
||
# tools/list
|
||
if method == "tools/list":
|
||
self._send_json(200, {"jsonrpc": "2.0", "id": req_id, "result": TOOLS_LIST})
|
||
return
|
||
|
||
# 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:
|
||
logger.error(f"Tool call error: {e}", exc_info=True)
|
||
self._send_json(200, {
|
||
"jsonrpc": "2.0",
|
||
"id": req_id,
|
||
"error": {"code": -32000, "message": str(e)}
|
||
})
|
||
return
|
||
|
||
self._send_json(400, {"error": "Unknown method: " + str(method)})
|
||
|
||
except Exception as e:
|
||
logger.error(f"POST error: {e}", exc_info=True)
|
||
try:
|
||
self.send_error(500, "Internal Server Error")
|
||
except Exception:
|
||
pass
|
||
|
||
def _call_tool(self, name: str, args: Dict[str, Any]) -> Any:
|
||
# General single-text tools
|
||
if name in (
|
||
"summarize_document",
|
||
"summarize_executive_brief",
|
||
"summarize_bullet_points",
|
||
"summarize_for_court",
|
||
"extract_key_points",
|
||
"extract_action_items",
|
||
"extract_entities",
|
||
"summarize_very_long_document"
|
||
):
|
||
text = args.get("text")
|
||
if not text:
|
||
raise ValueError("Text parameter is required")
|
||
max_length = args.get("max_length", 100)
|
||
final_output, chunks, intermediate_summaries = process_with_chunking(
|
||
text, name, max_length
|
||
)
|
||
doc_id = generate_doc_id()
|
||
store_document(doc_id, len(text), chunks, intermediate_summaries, final_output, name)
|
||
return {
|
||
"doc_id": doc_id,
|
||
"tool": name,
|
||
"result": final_output,
|
||
"metadata": {
|
||
"original_length": len(text),
|
||
"chunks": len(chunks)
|
||
}
|
||
}
|
||
|
||
# compare_documents
|
||
if name == "compare_documents":
|
||
text1 = args.get("text1")
|
||
text2 = args.get("text2")
|
||
if not text1 or not text2:
|
||
raise ValueError("text1 and text2 are required")
|
||
final_output, chunks, intermediate_summaries = compare_texts_with_chunking(text1, text2)
|
||
doc_id = generate_doc_id()
|
||
store_document(doc_id, len(text1) + len(text2), chunks, intermediate_summaries, final_output, name)
|
||
return {
|
||
"doc_id": doc_id,
|
||
"tool": name,
|
||
"result": final_output,
|
||
"metadata": {
|
||
"original_length_1": len(text1),
|
||
"original_length_2": len(text2),
|
||
"chunks": len(chunks)
|
||
}
|
||
}
|
||
|
||
# retrieve_document_data
|
||
if name == "retrieve_document_data":
|
||
doc_id = args.get("doc_id")
|
||
if not doc_id:
|
||
raise ValueError("doc_id is required")
|
||
doc = get_document(doc_id)
|
||
if not doc:
|
||
raise ValueError("Document not found or expired")
|
||
# Return metadata + final_output + intermediate_summaries (chunks on demand if needed)
|
||
return {
|
||
"doc_id": doc_id,
|
||
"tool_used": doc["tool_used"],
|
||
"final_output": doc["final_output"],
|
||
"intermediate_summaries": doc["intermediate_summaries"],
|
||
"metadata": {
|
||
"text_length": doc["text_length"],
|
||
"chunks_count": doc["chunks_count"],
|
||
"created_at": doc["created_at"]
|
||
}
|
||
}
|
||
|
||
# query_stored_document
|
||
if name == "query_stored_document":
|
||
doc_id = args.get("doc_id")
|
||
question = args.get("question")
|
||
if not doc_id or not question:
|
||
raise ValueError("doc_id and question are required")
|
||
doc = get_document(doc_id)
|
||
if not doc:
|
||
raise ValueError("Document not found or expired")
|
||
answer = query_chunks(doc["chunks"], question)
|
||
return {
|
||
"doc_id": doc_id,
|
||
"question": question,
|
||
"answer": answer
|
||
}
|
||
|
||
# clear_document_cache
|
||
if name == "clear_document_cache":
|
||
DOCUMENT_STORE.clear()
|
||
return {"status": "ok", "message": "Document cache cleared."}
|
||
|
||
raise ValueError(f"Unknown tool: {name}")
|
||
|
||
|
||
def main():
|
||
port = int(sys.argv[1]) if len(sys.argv) > 1 else int(os.environ.get("PORT", "8080"))
|
||
logger.info(f"Starting MCP Summary Server on 0.0.0.0:{port}")
|
||
logger.info(f"Auth mode: {'Bearer (API_KEY set)' if API_KEY else 'none (API_KEY not set)'}")
|
||
logger.info(f"LLM URL: {OPENAPI_URL}")
|
||
logger.info(f"Model: {MODEL_NAME}")
|
||
logger.info(f"Cache: max_docs={MAX_STORED_DOCS}, ttl={CACHE_TTL_SECONDS}s")
|
||
server = HTTPServer(("0.0.0.0", port), MCPSummaryHandler)
|
||
try:
|
||
logger.info(f"MCP Summary Server listening on 0.0.0.0:{port}")
|
||
server.serve_forever()
|
||
except KeyboardInterrupt:
|
||
logger.info("Shutting down...")
|
||
server.server_close()
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main() |