{"id":"olafgeibig-knowledge-mcp","name":"knowledge-mcp","af_score":52.5,"security_score":39.2,"reliability_score":25.0,"what_it_does":"knowledge-mcp is a Python MCP server plus CLI for creating, managing, and querying local knowledge bases backed by LightRAG (hybrid vector + knowledge graph retrieval). Users configure an embedding/LLM provider (e.g., OpenAI), ingest documents into per-KB directories, and query the resulting index via an MCP (FastMCP) interface for use by MCP-capable AI clients.","best_when":"You want a local MCP-based RAG tool that an AI desktop/IDE client can query, with per-knowledge-base retrieval configuration and custom formatting.","avoid_when":"You need a fully managed, internet-accessible service with strong built-in access control, SLAs, and clear rate-limit guarantees.","last_evaluated":"2026-03-30T15:25:33.709150+00:00","has_mcp":true,"has_api":false,"auth_methods":["API keys for upstream LLM/embedding providers via environment variables (e.g., OPENAI_API_KEY) used by LightRAG"],"has_free_tier":false,"known_gotchas":["MCP server appears to be launched as a CLI command (uvx/uv/docker). Agents must ensure the correct config/base args and mounted knowledge directory.","Indexing/ingestion uses external LLM/embedding providers; long-running operations may require timeouts/retries on the client side even if not documented.","Per-KB config (mode/top_k/token limits/user_prompt) affects retrieval/response; inconsistent config can lead to unexpected results."],"error_quality":0.0}