190 lines
6.5 KiB
Python
190 lines
6.5 KiB
Python
"""
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Local MCP server for use with Claude Code (stdio transport).
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This runs a fully local Hindsight instance with embedded PostgreSQL (pg0).
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No external database or server required.
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Run with:
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hindsight-local-mcp
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Or with uvx:
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uvx hindsight-api@latest hindsight-local-mcp
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Configure in Claude Code's MCP settings:
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{
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"mcpServers": {
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"hindsight": {
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"command": "uvx",
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"args": ["hindsight-api@latest", "hindsight-local-mcp"],
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"env": {
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"HINDSIGHT_API_LLM_API_KEY": "your-openai-key"
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}
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}
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}
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}
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Environment variables:
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HINDSIGHT_API_LLM_API_KEY: Required. API key for LLM provider.
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HINDSIGHT_API_LLM_PROVIDER: Optional. LLM provider (default: "openai").
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HINDSIGHT_API_LLM_MODEL: Optional. LLM model (default: "gpt-4o-mini").
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HINDSIGHT_API_MCP_LOCAL_BANK_ID: Optional. Memory bank ID (default: "mcp").
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HINDSIGHT_API_LOG_LEVEL: Optional. Log level (default: "info").
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"""
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import logging
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import os
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import sys
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from mcp.server.fastmcp import FastMCP
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from hindsight_api.config import (
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DEFAULT_MCP_LOCAL_BANK_ID,
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ENV_MCP_LOCAL_BANK_ID,
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)
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# Configure logging - default to info
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_log_level_str = os.environ.get("HINDSIGHT_API_LOG_LEVEL", "info").lower()
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_log_level_map = {
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"critical": logging.CRITICAL,
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"error": logging.ERROR,
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"warning": logging.WARNING,
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"info": logging.INFO,
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"debug": logging.DEBUG,
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}
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logging.basicConfig(
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level=_log_level_map.get(_log_level_str, logging.WARNING),
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format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
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stream=sys.stderr, # MCP uses stdout for protocol, logs go to stderr
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)
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logger = logging.getLogger(__name__)
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def create_local_mcp_server(bank_id: str, memory=None) -> FastMCP:
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"""
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Create a stdio MCP server with retain/recall tools.
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Args:
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bank_id: The memory bank ID to use for all operations.
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memory: Optional MemoryEngine instance. If not provided, creates one with pg0.
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Returns:
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Configured FastMCP server instance.
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"""
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# Import here to avoid slow startup if just checking --help
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from hindsight_api import MemoryEngine
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from hindsight_api.engine.memory_engine import Budget
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from hindsight_api.engine.response_models import VALID_RECALL_FACT_TYPES
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# Create memory engine with pg0 embedded database if not provided
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if memory is None:
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memory = MemoryEngine(db_url="pg0://hindsight-mcp")
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mcp = FastMCP("hindsight")
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@mcp.tool()
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async def retain(content: str, context: str = "general") -> dict:
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"""
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Store important information to long-term memory.
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Use this tool PROACTIVELY whenever the user shares:
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- Personal facts, preferences, or interests
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- Important events or milestones
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- User history, experiences, or background
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- Decisions, opinions, or stated preferences
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- Goals, plans, or future intentions
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- Relationships or people mentioned
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- Work context, projects, or responsibilities
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Args:
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content: The fact/memory to store (be specific and include relevant details)
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context: Category for the memory (e.g., 'preferences', 'work', 'hobbies', 'family'). Default: 'general'
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"""
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import asyncio
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async def _retain():
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try:
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await memory.retain_batch_async(bank_id=bank_id, contents=[{"content": content, "context": context}])
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except Exception as e:
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logger.error(f"Error storing memory: {e}", exc_info=True)
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# Fire and forget - don't block on memory storage
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asyncio.create_task(_retain())
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return {"status": "accepted", "message": "Memory storage initiated"}
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@mcp.tool()
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async def recall(query: str, max_tokens: int = 4096, budget: str = "low") -> dict:
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"""
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Search memories to provide personalized, context-aware responses.
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Use this tool PROACTIVELY to:
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- Check user's preferences before making suggestions
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- Recall user's history to provide continuity
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- Remember user's goals and context
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- Personalize responses based on past interactions
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Args:
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query: Natural language search query (e.g., "user's food preferences", "what projects is user working on")
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max_tokens: Maximum tokens to return in results (default: 4096)
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budget: Search budget level - "low", "mid", or "high" (default: "low")
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"""
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try:
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# Map string budget to enum
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budget_map = {"low": Budget.LOW, "mid": Budget.MID, "high": Budget.HIGH}
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budget_enum = budget_map.get(budget.lower(), Budget.LOW)
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search_result = await memory.recall_async(
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bank_id=bank_id,
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query=query,
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fact_type=list(VALID_RECALL_FACT_TYPES),
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budget=budget_enum,
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max_tokens=max_tokens,
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)
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return search_result.model_dump()
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except Exception as e:
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logger.error(f"Error searching: {e}", exc_info=True)
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return {"error": str(e), "results": []}
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return mcp
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async def _initialize_and_run(bank_id: str):
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"""Initialize memory and run the MCP server."""
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from hindsight_api import MemoryEngine
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# Create and initialize memory engine with pg0 embedded database
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print("Initializing memory engine...", file=sys.stderr)
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memory = MemoryEngine(db_url="pg0://hindsight-mcp")
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await memory.initialize()
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print("Memory engine initialized.", file=sys.stderr)
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# Create and run the server
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mcp = create_local_mcp_server(bank_id, memory=memory)
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await mcp.run_stdio_async()
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def main():
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"""Main entry point for the stdio MCP server."""
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import asyncio
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from hindsight_api.config import ENV_LLM_API_KEY, get_config
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# Check for required environment variables
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config = get_config()
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if not config.llm_api_key:
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print(f"Error: {ENV_LLM_API_KEY} environment variable is required", file=sys.stderr)
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print("Set it in your MCP configuration or shell environment", file=sys.stderr)
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sys.exit(1)
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# Get bank ID from environment, default to "mcp"
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bank_id = os.environ.get(ENV_MCP_LOCAL_BANK_ID, DEFAULT_MCP_LOCAL_BANK_ID)
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# Print startup message to stderr (stdout is reserved for MCP protocol)
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print(f"Hindsight MCP server starting (bank_id={bank_id})...", file=sys.stderr)
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# Run the async initialization and server
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asyncio.run(_initialize_and_run(bank_id))
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if __name__ == "__main__":
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main()
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