{"id":"featureform-enrichmcp","name":"enrichmcp","af_score":60.5,"security_score":52.8,"reliability_score":38.8,"what_it_does":"EnrichMCP is a Python framework for building MCP (Model Context Protocol) servers that expose a semantic, type-safe data layer. It generates typed/discoverable tools from data models (e.g., SQLAlchemy models) or user-defined Pydantic schemas, supports relationships, validation, pagination, caching/context, and can serve over multiple transports (stdio by default, plus streamable HTTP).","best_when":"You want AI agents to reliably explore and query your structured data model with strong typing/validation and relationship navigation, and you’re comfortable running an MCP server in your own environment.","avoid_when":"You need a fully managed, turnkey SaaS with guaranteed auth/rate limiting features out-of-the-box, or you cannot run/host the MCP server code.","last_evaluated":"2026-03-30T13:26:19.240131+00:00","has_mcp":true,"has_api":false,"auth_methods":["Application-level authorization via MCP client context (e.g., ctx.get('authenticated_user_id'))"],"has_free_tier":false,"known_gotchas":["If using streamable HTTP transport, ensure your MCP client/agent supports the selected transport and streaming behavior.","Authorization is not automatic in the framework materials shown; resolvers should enforce permissions using context provided by the MCP client.","When enabling server-side LLM sampling, ensure you understand client-side billing/LLM selection behavior and constrain tool usage via MCP sampling options if supported."],"error_quality":0.0}