A
licenseB
qualityD
maintenanceEnables retrieval-augmented generation by embedding queries with a chosen provider (e.g., OpenAI) and searching supported vector stores (Pinecone, pgvector) to return relevant content.
1
Apache 2.0
72,431 servers. Updated
Search Relevance ↓
Recent Usage ↓
Date Added ↓
Date Updated ↓
Weekly Downloads ↓
GitHub Stars ↓
Recent GitHub Stars ↓
Matching MCP tools:
Matching MCP Connectors:
io.github.sercanmetalore
io.github.aa0101181514
io.github.maxi-moss
com.ai-rete-rag
"Information about RAG (Retrieval-Augmented Generation) or rag-related topics" matching MCP servers:
- Flicense-qualityBmaintenanceAn MCP server that retrieves relevant PDF chunks via local embeddings and returns them to IDE agents (Cursor, Kiro, Claude Code) for answer generation.
- AlicenseBqualityDmaintenanceEnhances AI model capabilities with structured, retrieval-augmented thinking processes that enable dynamic thought chains, parallel exploration paths, and recursive refinement cycles for improved reasoning.124MIT
- AlicenseAqualityBmaintenanceMinimal RAG-over-a-corpus MCP retrieval: search_knowledge returns cited chunks. Local embeddings.1MIT
- AlicenseBqualityDmaintenanceA 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.11131MIT
- Alicense-qualityDmaintenanceA server that integrates Retrieval-Augmented Generation (RAG) with the Model Control Protocol (MCP) to provide web search capabilities and document analysis for AI assistants.4Apache 2.0
- Alicense-qualityDmaintenanceAn MCP server that indexes documents and serves relevant context to LLMs via Retrieval Augmented Generation (RAG).11536MIT
- Alicense-qualityCmaintenanceEnables AI assistants to search and retrieve information from your knowledge base using RAG (Retrieval-Augmented Generation) with hybrid search, document indexing, and ChromaDB vector storage.115MIT
- Alicense-qualityDmaintenanceA Retrieval Augmented Generation system that enables AI assistants to perform semantic searches and manage document indices for markdown files. It supports PostgreSQL with pgvector and integrates both Google Gemini and Ollama for intelligent embedding generation.1MIT
- Alicense-qualityCmaintenanceEnables retrieval-augmented generation over a local markdown corpus, allowing grounded, cited answers via an MCP tool or CLI.13MIT
- Alicense-qualityFmaintenanceA Model Context Protocol (MCP) server with Retrieval-Augmented Generation (RAG) for answering questions about imaginary SuperNova documentation. Enables semantic search over documentation using HuggingFace embeddings.1MIT
- Alicense-qualityCmaintenanceAn MCP-compatible system that handles large files (up to 200MB) with intelligent chunking and multi-format document support for advanced retrieval-augmented generation.10MIT
- Flicense-qualityDmaintenanceA Retrieval Augmented Generation MCP server that ingests documents into a local vector database and enables semantic search queries.10
- Flicense-qualityBmaintenanceAn MCP-based multi-agent retrieval-augmented generation system that enables question answering over academic papers with hybrid search, knowledge graph multi-hop reasoning, and source-cited answers.
- Flicense-qualityDmaintenanceConnects RAG knowledge bases to Claude Desktop and Claude Code, allowing retrieval-augmented generation from ingested documents.
- Flicense-qualityBmaintenanceHybrid RAG over Claude Code and Hermes session history via MCP tools for search, ingest, and context injection.
- Flicense-qualityBmaintenanceA CLI tool and MCP server that turns markdown documentation into a searchable, queryable knowledge base.280
- Flicense-qualityCmaintenanceMCP server for Fathom Works RAG that exposes a self-hosted knowledge base as tools for any MCP-capable LLM to query documents, manage libraries, and ingest files or URLs.
- Flicense-qualityDmaintenanceEnables RAG (Retrieval-Augmented Generation) capabilities with document processing, vector storage, and intelligent Q\&A using OpenAI embeddings and semantic search.
- Flicense-qualityDmaintenanceImplements Retrieval-Augmented Generation (RAG) using GroundX and OpenAI, allowing users to ingest documents and perform semantic searches with advanced context handling through Modern Context Processing (MCP).5