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MCP Integration Summary

Overview

Successfully integrated Model Context Protocol (MCP) support into the haive-dataflow package, enabling seamless discovery, registration, and management of MCP servers and their capabilities.

What Was Implemented

1. MCP Models and Types

  • Added comprehensive MCP models to registry/models.py:
    • MCPTransport - Enum for transport types (stdio, http, sse)
    • MCPServerConfig - Server configuration model
    • MCPToolDefinition - Tool definition model
    • MCPResourceDefinition - Resource definition model
    • MCPPromptDefinition - Prompt template model
    • MCPServerHealth - Health monitoring model
  • Added MCP entity types to the registry system

2. MCP Discovery Module (mcp/discovery.py)

  • Comprehensive MCP server discovery from multiple sources:
    • npm packages (@modelcontextprotocol/*)
    • PyPI packages (mcp-*)
    • GitHub repositories
    • Local haive-mcp downloaded servers
  • Async discovery with proper error handling
  • Integration with registry system for automatic registration

3. MCP Client Module (mcp/client.py)

  • MCPClient - Main client for managing MCP server connections
  • MCPToolProvider - Provider for registering MCP tools in dataflow
  • MCPServerAdapter - Adapter for individual server management
  • Integration with LangChain MCP adapters for tool loading
  • Support for multiple transport types

4. Health Monitoring (mcp/health.py)

  • MCPHealthMonitor - Comprehensive health monitoring service
  • MCPHealthChecker - Individual server health checking
  • Automatic recovery attempts for failed servers
  • Performance metrics tracking

5. Registry Integration

  • MCP servers can be discovered and registered in the dataflow registry
  • Full integration with existing discovery mechanisms
  • Support for querying MCP servers by type

6. Dependencies

  • Added mcp ^1.10.1 for official MCP SDK support
  • Added langchain-mcp-adapters ^0.1.0 for LangChain integration

Testing

Created comprehensive tests in haive-mcp/tests/:

  • test_mcp_models.py - Tests for all MCP model types
  • test_mcp_integration.py - Integration tests with registry

Usage Example

from haive.dataflow import registry_system, MCPServerConfig, MCPTransport
from haive.dataflow.mcp import MCPClient

# Register an MCP server
config = MCPServerConfig(
    name="filesystem-server",
    transport=MCPTransport.STDIO,
    command="mcp-filesystem",
    capabilities=["read", "write"]
)

# Create MCP client
client = MCPClient(registry_system)
await client.initialize_from_registry()

# Get available tools
tools = await client.get_available_tools()

# Execute a tool
result = await client.execute_tool("read_file", {"path": "/path/to/file"})

Next Steps

  1. Create REST API endpoints for MCP functionality
  2. Add MCP server management UI
  3. Implement automatic MCP server installation
  4. Add more comprehensive health monitoring
  5. Create documentation for end users

Status

✅ Core MCP integration is complete and functional ✅ All tests passing ✅ Ready for API endpoint development