Enterprise Internal Knowledge Base: Production-Ready RAG + MCP runs on your own machine — the client starts it, so there is no endpoint to ping. Last commit 26 May 2026.
Production-ready RAG + MCP demo: eval-in-CI merge gate, Langfuse traces, structure-aware chunking.
We read the source, 20 h ago · rules 3dff92dd89df
What this server is able to do. For an MCP server this is often the job itself — a terminal server runs commands because that is what it is for. Listed so you know what you are plugging in, not as an accusation.
<li><span style="font-size: 16px"><span style="font-family: Arial, Helvetica, sans-serif"><span style="line-height: 115%">Update the “*35% Exemption” field using the dropdown menu. </span></span></span></li>
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Is this your server and something here is wrong? Tell us — corrections are free and do not require a plan.
We found places where it runs commands, builds paths or queries from values it is given. None of that is a flaw by itself — it becomes one when the code changes, and code changes quietly between releases. We re-read it on every one.
Turn documents into structured, AI-ready data by parsing, enriching, chunking, and embedding.
Text extraction, keyword extraction, language detection, and chunking for RAG
Turn any document into a production-ready RAG dataset from Claude, Cursor, or any MCP host.
Minimal RAG-over-a-corpus MCP retrieval: search_knowledge returns cited chunks. Local embeddings.
Academic paper knowledge-base MCP server: PDF ingest, vector search with reranking, KB management.
Offline codebase knowledge graph: 91% token reduction vs naive RAG, plus cross-session agent memory.
Governed graph-native agent memory: knowledge extraction, fusion, hybrid RAG, scoped access tokens.
Rag Knowledge Graph MCP Server by MEOK AI Labs
Answers built from our own checks of this server.