"An MCP to access OpenAI's RAG system via API" matching MCP servers:

Enables routing context and execution across AI tools like Claude, Cursor, Windsurf, and ChatGPT with a shared memory, task board, and context bus, plus local file conversion.

MCP server enabling LLMs to query a local Calibre Content Server for ebook metadata, chapters, and content in HTML or Markdown.

Local RAG system for Claude Code with hybrid search (semantic + BM25), cross-encoder reranking, markdown-aware chunking, and 12 MCP tools. Zero external servers, pure ONNX in-process.

Enables indexing local documents (PDF, Markdown, text, code) into a knowledge base and querying them via semantic search using local embeddings, all running privately on your machine.

Enables semantic search and question answering over a knowledge base using hybrid retrieval and grounded answers, all running offline with no API keys.

Enables coding agents to retrieve project context from an AnythingLLM RAG at session start and write back human-approved durable facts via five workflow-shaped tools.

Intelligent knowledge base system that enables users to process documents in 25+ formats, perform semantic search and Q\&A through vector retrieval. Supports multiple AI models including OpenAI and DouBao with local processing capabilities.

Offline RAG MCP server that indexes documents into SQLite with hybrid vector and lexical search, and enables natural language queries via Claude.

About Your task manager is the best agent memory you're not using. An agent-native context layer over your existing task app.

A complete MCP server for Retrieval-Augmented Generation with file management and vector memory for agents. Supports multiple document formats (PDF, DOCX, TXT, MD, CSV, JSON) with semantic search using Hugging Face embeddings and ChromaDB for efficient vector storage.

Local-first knowledge system for reasoning agents, exposing facts, evidence, documents, retrieval, and audit history through a thin stdio MCP server.

Enables chat assistants to remember past conversations by saving each turn and retrieving semantically relevant memories via similarity search. Also allows fetching all memories from a specific date.

Enables document-based Q&A with multi-modal RAG, hybrid retrieval, knowledge graph reasoning, and multi-agent orchestration via MCP tools.

MCP server providing RAG context and failure capture for Claude Code, enabling semantic search across project knowledge and storing/analyzing failures.

RAG Knowledge - MCP server providing AI-powered tools and automation by MEOK AI Labs

Enables LLMs to perform conceptual search over local PDF/EPUB documents using a RAG pipeline with corpus-driven concept extraction and WordNet enrichment.

Enables retrieval-augmented generation over a local markdown corpus, allowing grounded, cited answers via an MCP tool or CLI.

Hybrid RAG pipeline that indexes documents and exposes them via an MCP server, enabling natural language queries to retrieve relevant context chunks for LLMs.

Enables MCP clients to manage Wiki.js pages, users, and groups via CRUD tools and to perform semantic RAG search over wiki content with hybrid vector and lexical retrieval.

Enables AI agents to use a hybrid dense+sparse RAG system with tag-based long-term memory (importance, knowledge-type decay, access-frequency boost) via MCP tools for document ingestion, hybrid search, staged reranking, forgetting, and index management.