Add MCP HTTP/SSE server and complete n8n integration
Major Changes: - Implemented MCP HTTP/SSE transport server for n8n and web clients - Created mcp_server/http_server.py with FastAPI for JSON-RPC 2.0 over HTTP - Added health check endpoint (/health) for container monitoring - Refactored mcp-server/ to mcp_server/ (Python module structure) - Updated Dockerfile.mcp to run HTTP server with health checks MCP Server Features: - 7 memory tools exposed via MCP (add, search, get, update, delete) - HTTP/SSE transport on port 8765 for n8n integration - stdio transport for Claude Code integration - JSON-RPC 2.0 protocol implementation - CORS support for web clients n8n Integration: - Successfully tested with AI Agent workflows - MCP Client Tool configuration documented - Working webhook endpoint tested and verified - System prompt optimized for automatic user_id usage Documentation: - Created comprehensive Mintlify documentation site - Added docs/mcp/introduction.mdx - MCP server overview - Added docs/mcp/installation.mdx - Installation guide - Added docs/mcp/tools.mdx - Complete tool reference - Added docs/examples/n8n.mdx - n8n integration guide - Added docs/examples/claude-code.mdx - Claude Code setup - Updated README.md with MCP HTTP server info - Updated roadmap to mark Phase 1 as complete Bug Fixes: - Fixed synchronized delete operations across Supabase and Neo4j - Updated memory_service.py with proper error handling - Fixed Neo4j connection issues in delete operations Configuration: - Added MCP_HOST and MCP_PORT environment variables - Updated .env.example with MCP server configuration - Updated docker-compose.yml with MCP container health checks Testing: - Added test scripts for MCP HTTP endpoint verification - Created test workflows in n8n - Verified all 7 memory tools working correctly - Tested synchronized operations across both stores Version: 1.0.0 Status: Phase 1 Complete - Production Ready 🤖 Generated with Claude Code Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
Claude Code
373
mcp_server/tools.py
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373
mcp_server/tools.py
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"""
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MCP tools for T6 Mem0 v2
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Tool definitions and handlers
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"""
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import logging
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from typing import Any, Dict, List
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from mcp.types import Tool, TextContent
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from mem0 import Memory
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from memory_cleanup import MemoryCleanup
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logger = logging.getLogger(__name__)
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class MemoryTools:
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"""MCP tools for memory operations"""
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def __init__(self, memory: Memory):
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"""
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Initialize memory tools
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Args:
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memory: Mem0 instance
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"""
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self.memory = memory
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self.cleanup = MemoryCleanup(memory)
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def get_tool_definitions(self) -> List[Tool]:
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"""
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Get MCP tool definitions
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Returns:
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List of Tool definitions
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"""
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return [
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Tool(
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name="add_memory",
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description="Add new memory from messages. Extracts and stores important information from conversation.",
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inputSchema={
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"type": "object",
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"properties": {
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"messages": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"role": {"type": "string", "enum": ["user", "assistant", "system"]},
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"content": {"type": "string"}
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},
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"required": ["role", "content"]
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},
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"description": "Conversation messages to extract memory from"
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},
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"user_id": {
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"type": "string",
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"description": "User identifier (optional)"
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},
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"agent_id": {
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"type": "string",
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"description": "Agent identifier (optional)"
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},
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"metadata": {
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"type": "object",
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"description": "Additional metadata (optional)"
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}
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},
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"required": ["messages"]
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}
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),
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Tool(
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name="search_memories",
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description="Search memories by semantic similarity. Find relevant memories based on a query.",
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inputSchema={
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"type": "object",
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"properties": {
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"query": {
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"type": "string",
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"description": "Search query"
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},
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"user_id": {
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"type": "string",
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"description": "Filter by user ID (optional)"
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},
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"agent_id": {
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"type": "string",
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"description": "Filter by agent ID (optional)"
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},
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"limit": {
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"type": "integer",
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"minimum": 1,
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"maximum": 50,
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"default": 10,
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"description": "Maximum number of results"
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}
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},
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"required": ["query"]
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}
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),
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Tool(
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name="get_memory",
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description="Get a specific memory by its ID",
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inputSchema={
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"type": "object",
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"properties": {
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"memory_id": {
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"type": "string",
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"description": "Memory identifier"
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}
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},
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"required": ["memory_id"]
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}
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),
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Tool(
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name="get_all_memories",
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description="Get all memories for a user or agent",
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inputSchema={
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"type": "object",
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"properties": {
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"user_id": {
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"type": "string",
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"description": "User identifier (optional)"
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},
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"agent_id": {
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"type": "string",
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"description": "Agent identifier (optional)"
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}
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}
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}
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),
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Tool(
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name="update_memory",
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description="Update an existing memory's content",
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inputSchema={
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"type": "object",
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"properties": {
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"memory_id": {
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"type": "string",
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"description": "Memory identifier"
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},
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"data": {
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"type": "string",
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"description": "New memory content"
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}
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},
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"required": ["memory_id", "data"]
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}
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),
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Tool(
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name="delete_memory",
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description="Delete a specific memory by ID",
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inputSchema={
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"type": "object",
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"properties": {
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"memory_id": {
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"type": "string",
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"description": "Memory identifier"
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}
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},
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"required": ["memory_id"]
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}
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),
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Tool(
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name="delete_all_memories",
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description="Delete all memories for a user or agent. Use with caution!",
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inputSchema={
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"type": "object",
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"properties": {
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"user_id": {
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"type": "string",
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"description": "User identifier (optional)"
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},
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"agent_id": {
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"type": "string",
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"description": "Agent identifier (optional)"
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}
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}
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}
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)
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]
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async def handle_add_memory(self, arguments: Dict[str, Any]) -> List[TextContent]:
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"""Handle add_memory tool call"""
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try:
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messages = arguments.get("messages", [])
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user_id = arguments.get("user_id")
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agent_id = arguments.get("agent_id")
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metadata = arguments.get("metadata", {})
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result = self.memory.add(
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messages=messages,
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user_id=user_id,
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agent_id=agent_id,
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metadata=metadata
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)
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memories = result.get('results', [])
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response = f"Successfully added {len(memories)} memory(ies):\n\n"
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for mem in memories:
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response += f"- {mem.get('memory', mem.get('data', 'N/A'))}\n"
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response += f" ID: {mem.get('id', 'N/A')}\n\n"
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return [TextContent(type="text", text=response)]
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except Exception as e:
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logger.error(f"Error adding memory: {e}")
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return [TextContent(type="text", text=f"Error: {str(e)}")]
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async def handle_search_memories(self, arguments: Dict[str, Any]) -> List[TextContent]:
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"""Handle search_memories tool call"""
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try:
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query = arguments.get("query")
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user_id = arguments.get("user_id")
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agent_id = arguments.get("agent_id")
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limit = arguments.get("limit", 10)
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result = self.memory.search(
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query=query,
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user_id=user_id,
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agent_id=agent_id,
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limit=limit
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)
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# In mem0 v0.1.118+, search returns dict with 'results' key
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memories = result.get('results', []) if isinstance(result, dict) else result
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if not memories:
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return [TextContent(type="text", text="No memories found matching your query.")]
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response = f"Found {len(memories)} relevant memory(ies):\n\n"
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for i, mem in enumerate(memories, 1):
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# Handle both string and dict responses
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if isinstance(mem, str):
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response += f"{i}. {mem}\n\n"
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elif isinstance(mem, dict):
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response += f"{i}. {mem.get('memory', mem.get('data', 'N/A'))}\n"
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response += f" ID: {mem.get('id', 'N/A')}\n"
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if 'score' in mem:
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response += f" Relevance: {mem['score']:.2%}\n"
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response += "\n"
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else:
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response += f"{i}. {str(mem)}\n\n"
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return [TextContent(type="text", text=response)]
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except Exception as e:
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logger.error(f"Error searching memories: {e}")
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return [TextContent(type="text", text=f"Error: {str(e)}")]
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async def handle_get_memory(self, arguments: Dict[str, Any]) -> List[TextContent]:
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"""Handle get_memory tool call"""
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try:
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memory_id = arguments.get("memory_id")
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memory = self.memory.get(memory_id=memory_id)
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if not memory:
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return [TextContent(type="text", text=f"Memory not found: {memory_id}")]
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response = f"Memory Details:\n\n"
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response += f"ID: {memory.get('id', 'N/A')}\n"
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response += f"Content: {memory.get('memory', memory.get('data', 'N/A'))}\n"
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if memory.get('user_id'):
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response += f"User ID: {memory['user_id']}\n"
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if memory.get('agent_id'):
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response += f"Agent ID: {memory['agent_id']}\n"
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if memory.get('metadata'):
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response += f"Metadata: {memory['metadata']}\n"
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return [TextContent(type="text", text=response)]
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except Exception as e:
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logger.error(f"Error getting memory: {e}")
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return [TextContent(type="text", text=f"Error: {str(e)}")]
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async def handle_get_all_memories(self, arguments: Dict[str, Any]) -> List[TextContent]:
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"""Handle get_all_memories tool call"""
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try:
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user_id = arguments.get("user_id")
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agent_id = arguments.get("agent_id")
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result = self.memory.get_all(
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user_id=user_id,
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agent_id=agent_id
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)
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# In mem0 v0.1.118+, get_all returns dict with 'results' key
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memories = result.get('results', []) if isinstance(result, dict) else result
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if not memories:
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return [TextContent(type="text", text="No memories found.")]
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response = f"Retrieved {len(memories)} memory(ies):\n\n"
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for i, mem in enumerate(memories, 1):
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# Handle both string and dict responses
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if isinstance(mem, str):
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response += f"{i}. {mem}\n\n"
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elif isinstance(mem, dict):
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response += f"{i}. {mem.get('memory', mem.get('data', 'N/A'))}\n"
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response += f" ID: {mem.get('id', 'N/A')}\n\n"
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else:
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response += f"{i}. {str(mem)}\n\n"
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return [TextContent(type="text", text=response)]
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except Exception as e:
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logger.error(f"Error getting all memories: {e}")
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return [TextContent(type="text", text=f"Error: {str(e)}")]
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async def handle_update_memory(self, arguments: Dict[str, Any]) -> List[TextContent]:
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"""Handle update_memory tool call"""
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try:
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memory_id = arguments.get("memory_id")
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data = arguments.get("data")
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result = self.memory.update(
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memory_id=memory_id,
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data=data
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)
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response = f"Memory updated successfully:\n\n"
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response += f"ID: {result.get('id', memory_id)}\n"
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response += f"New Content: {data}\n"
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return [TextContent(type="text", text=response)]
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except Exception as e:
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logger.error(f"Error updating memory: {e}")
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return [TextContent(type="text", text=f"Error: {str(e)}")]
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async def handle_delete_memory(self, arguments: Dict[str, Any]) -> List[TextContent]:
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"""Handle delete_memory tool call"""
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try:
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memory_id = arguments.get("memory_id")
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self.memory.delete(memory_id=memory_id)
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return [TextContent(type="text", text=f"Memory {memory_id} deleted successfully.")]
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except Exception as e:
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logger.error(f"Error deleting memory: {e}")
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return [TextContent(type="text", text=f"Error: {str(e)}")]
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async def handle_delete_all_memories(self, arguments: Dict[str, Any]) -> List[TextContent]:
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"""
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Handle delete_all_memories tool call
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IMPORTANT: Uses synchronized deletion to ensure both
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Supabase (vector store) and Neo4j (graph store) are cleaned up.
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"""
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try:
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user_id = arguments.get("user_id")
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agent_id = arguments.get("agent_id")
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# Use synchronized deletion to clean up both Supabase and Neo4j
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result = self.cleanup.delete_all_synchronized(
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user_id=user_id,
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agent_id=agent_id
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)
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filter_str = f"user_id={user_id}" if user_id else f"agent_id={agent_id}" if agent_id else "all filters"
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response = f"All memories deleted for {filter_str}.\n"
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response += f"Supabase: {'✓' if result['supabase_success'] else '✗'}, "
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response += f"Neo4j: {result['neo4j_nodes_deleted']} nodes deleted"
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return [TextContent(type="text", text=response)]
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except Exception as e:
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logger.error(f"Error deleting all memories: {e}")
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return [TextContent(type="text", text=f"Error: {str(e)}")]
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