This project implements a Model Context Protocol (MCP) server that enables AI models to perform semantic search and intelligent document retrieval from local or remote filesystems. The server facilitates RAG (Retrieval-Augmented Generation) workflows, enhancing AI context by retrieving information from diverse sources.

Deterministic, namespace-isolated document retrieval with a CLI and MCP server.

CrateDB MCP Server for natural-language Text-to-SQL and documentation retrieval.

Wenmai Search — self-hosted, traceable hybrid document retrieval with REST, MCP, and Web UI

An intelligent Model Context Protocol (MCP) server for Azure AI Search integration with Claude Desktop - Transform enterprise document search into natural AI conversations using LangGraph workflows, Google Gemini, and advanced retrieval-augmented generation (RAG).

Self-hosted retrieval infrastructure for AI assistants. MCP-first search and grounded answers over a private corpus, with citations that resolve to a real location in a real document — a page, a heading, a line, a cell.

Indexes documents on your computer so AI agents can retrieve only the relevant, source-located passages.

A lightweight Model Context Protocol (MCP) server that brings RAG (Retrieval-Augmented Generation) capabilities to your LLM over Markdown documentation stored on S3.

Retrieval-Augmented Generation (RAG) based Document Assistant that enables users to upload PDF documents and ask natural language questions about their content.

DocOrbit discovers authoritative documentation, resolves it against your project's dependency versions, retrieves task-specific context, and verifies generated code against documentation contracts.

A FastMCP-based document server that exposes files as MCP resources and tools for AI-powered document retrieval and search.

This is a local rag with retrieval-augmented experience log for coding with indexed framework, reranking documents, emdedding-normalization

MCP server for documentation retrieval with token-aware content condensation

AI-powered documentation RAG system with MCP server for Claude Code. Search and retrieve technical documentation on-demand with vector embeddings and smart web scraping.

Enterprise-grade documentation intelligence for AI coding assistants using the Model Context Protocol (MCP). Index, synchronize, understand, and retrieve documentation from any source with blazing-fast semantic search and developer-friendly workflows.

Retrieve answers from enterprise documents with AI-powered RAG, grounded in cited sources.

MCP server for TextView — persistent memory for AI agents. Save, search, and retrieve documents across AI sessions.

A Model Context Protocol server that provides documentation access capabilities. This server enables LLMs to search and retrieve content from documentation websites by scraping them with crawl4ai. Built with FastMCP v2.

A Model Context Protocol server for fetching and storing documentation in a vector database, enabling semantic search and retrieval to augment LLM capabilities with relevant documentation context.

Read-only MCP server for searching and retrieving the official NightyScript documentation and examples.

MCP server for Alpine.js Template Outlet documentation - enables AI assistants to search and retrieve Template Outlet docs

MCP Server for multi-cloud provider documentation search — search and retrieve official cloud vendor docs directly in AI coding assistants

CrateDB MCP Server for natural-language Text-to-SQL and documentation retrieval.

This is a lightweight, plug-and-play MCP server that empowers any LLM to dynamically search and retrieve up-to-date documentation from popular AI libraries such as LangChain, LlamaIndex, and OpenAI.

Local semantic search over the documents on your own Mac. OKF knowledge bundle, hybrid retrieval, MCP server. Nothing leaves the machine.

Local-first MCP server for hybrid search across documents and Confluence. Local indexing, multilingual retrieval, and a Windows desktop companion with a combined installer.

Open-source retrieval for agentic RAG: document search with exact citations on your laptop or servers, with OCR, CLI, MCP, and TypeScript.

Local-first agentic RAG with citations - hybrid search, reranking and multimodal retrieval over your own documents, no database server required - Minimal dependencies

Documentation retrieval for AI agents: fetch the section you need, not the whole doc. Markdown, RST, AsciiDoc, Jupyter, HTML, OpenAPI and more. Runs locally, works with Claude Code and any MCP client. Free for non-commercial use; commercial use needs a one-time license.

Multi-tenant Model Context Protocol (MCP) server that gives AI agents a persistent, searchable, version-controlled document store — save in one session, retrieve and edit in another.

Local document retrieval server for PDF, TXT, and MD files. ragU does not generate answers — it returns context (retrieved passages) and sources (metadata) for your LLM to use.

MCP server over the Liara cloud documentation: hybrid Persian/English retrieval, page reads, deployment config and build-log diagnosis. Corpus ships in the package, so no ingest, no database and no API key.

Token-efficient MCP server for structured documentation retrieval via section-level indexing

Find the RAG retrieval setup that works on your data, then use it automatically. HyDE, Step-Back, Fan-Out, hybrid BM25 search and reranking, benchmarked on your documents (Markdown, HTML, PDF, DOCX) and routed per query, with citations and streaming, for

Turn documents, wikis, emails, and Confluence/SharePoint/OneDrive/Google Drive sources into governed, provenance-bearing knowledge AI agents can trust — reviewed publication, conflict retention, citation-exact retrieval. Native in Claude Code, Codex, Kiro, and any MCP client.

mcp-rag-server is a Model Context Protocol (MCP) server that enables Retrieval Augmented Generation (RAG) capabilities. It empowers Large Language Models (LLMs) to answer questions based on your document content by indexing and retrieving relevant information efficiently.

Search Czech university theses and retrieve document links for MCP clients.

Local-first, offline-capable Agent memory & knowledge service. REST + MCP dual-protocol, hybrid retrieval (vector + BM25/RRF), document/webpage RAG. No LLM required for storage & retrieval

An MCP server for retrieving and cleaning live, official documentation for AI tools.

RAG-Sandbox is a comprehensive, multi-tenant Agentic RAG (Retrieval-Augmented Generation) platform that redefines how users interact with their data. It moves beyond simple text retrieval to offer a fully multimodal, intelligent experience—transforming static documents into conversable agents, podcasts, and immersive stories.

LangChain-based MCP server example that indexes local Markdown and text files, splits them into searchable chunks, and exposes document search and retrieval tools over Streamable HTTP. The repo includes open-source-only dependencies, architecture documentation, automated tests, and verified coverage.

A vector search MCP for document retrieval using MongoDB Atlas Vector Search and Voyage AI Context embeddings.

A lightweight MCP server that integrates with Vertex AI Search to retrieve configurable snippets and extractive segments for document discovery.

A Model Context Protocol (MCP) server that provides AI assistants with the ability to search and retrieve Microsoft AutoGen documentation.

📖 A Retrieval-Augmented Generation (RAG) MCP server for markdown documentation with semantic search capabilities

The MCP Docs Server is a Node.js implementation of a Model Context Protocol (MCP) server that provides a powerful tool to search and retrieve up-to-date documentation snippets from popular AI/ML libraries. It leverages the Serper API to perform Google searches restricted to specific documentation domains, extracts meaningful content using web

Audrent's paid MCP server for U.S. Federal Register documents — search, retrieval, and watchlists for AI agents.

Local-first MCP server: hash-chained document store with signed retrieval receipts and an agent orient (staleness/drift) protocol.

Model Context Protocol (MCP) server that provides structured access to LayerChart documentation and source code. This server enables AI agents to easily retrieve, search, and understand LayerChart components and examples.

Integrate your applications with the Handwriting OCR service to effortlessly upload documents, check their processing status, and retrieve OCR results in Markdown format. Enhance your workflows by automating text extraction from images and PDFs with ease.

Context compression and planning for RAG and AI agents: fit retrieved documents, chat history and tool output into a token budget.

Multi-user MCP server for semantic search over personal document collections. Built with FastMCP, FastAPI, Qdrant, and React — features per-user isolation and measurable retrieval precision.

🤖 A multi-project RAG (Retrieval-Augmented Generation) platform that provides project-aware access to documentation and knowledge through a web dashboard with AI chat, CLI, and MCP server.

Local-first MCP server: hash-chained document store, signed retrieval receipts, and an orient protocol that tells your agent when its beliefs went stale.

🔍 Index and manage local documents with the MCP server, offering AI access through a unified platform for easy search and retrieval.

An experimental Model Context Protocol (MCP) server that allows LLM clients (like Claude Desktop) to dynamically retrieve the latest implementation details and documentation from popular AI libraries such as LangChain, LlamaIndex, and OpenAI.

Caelune (凯露恩) — Local-first retrieval for private Markdown, PDF, and Tika documents, with a Windows desktop app and read-only MCP server.｜本地优先的私人知识检索工具。

RagWiser is a Retrieval Augmented Generation (RAG) system built with Spring Boot that enables users to upload PDF documents, process them, and ask questions about their content using natural language.

MCP server for Pipecat voice AI framework documentation. Search 6,936 chunks from 317 pages via Claude Desktop or Cursor. Hybrid BM25 + FAISS retrieval with cross-encoder reranking. 78.8% MRR, 93% Recall@5, 36ms avg latency.

A powerful MCP (Model Context Protocol) server that provides RAG (Retrieval-Augmented Generation) capabilities with support for multiple document formats. Powered by LangChain, ChromaDB, with OpenAI or Ollama embeddings.