{"id":"rag-mcp-server","name":"rag-mcp-server","af_score":63.8,"security_score":22.8,"reliability_score":21.2,"what_it_does":"Provides an MCP server that builds and queries a local Retrieval-Augmented Generation (RAG) knowledge base from document directories. It extracts text from .txt and .pdf files, chunks content, computes embeddings (SentenceTransformers), indexes vectors with FAISS, and stores document metadata in SQLite; exposes MCP tools for initializing, searching, refreshing, getting stats, and listing documents.","best_when":"You want an offline/local MCP integration to index and retrieve from a local document corpus using an AI agent.","avoid_when":"You need strong authentication/authorization, network-accessed service security, or cloud-managed guarantees (SLA/compliance, audit trails).","last_evaluated":"2026-04-04T21:46:26.368610+00:00","has_mcp":true,"has_api":false,"auth_methods":[],"has_free_tier":false,"known_gotchas":["No explicit guidance on concurrency (multiple agents/processes updating the same knowledge base)","Tool behavior around missing/empty knowledge bases (e.g., calling semantic_search before initialize) isn’t documented in detail","No documented rate limiting for tool calls (agents may need to implement backoff themselves)","Large PDFs may be slow; progress bars exist but agent-facing guidance for long-running operations is not specified"],"error_quality":0.0}