"Search for information about 'rag'" matching MCP servers:

Enables natural language search over indexed codebases with source citations and incremental indexing.

Enables agent tools like Claude Code and GitHub Copilot to perform knowledge retrieval using hybrid search (BM25 + dense) with reranking, via MCP protocol.

An MCP (Model Context Protocol) server that gives AI agents live, structured ad intelligence across Facebook, Google, and Instagram — data that no base model can produce from training alone. Powered by Apify actors. Works with any MCP-compatible client: Cursor, Claude, etc.

A Model Context Protocol server that provides web search capabilities using Google Custom Search API and webpage content extraction functionality.

Enables AI agents to run semantic search over a local folder of text files, returning the best-matching passages ranked by cosine similarity, each with its source filename and score. Runs locally with no API keys, external services, or vector database.

MCP server that gives AI agents access to current web information through web_search and fetch_url tools, using Tavily, Brave, and DuckDuckGo providers with automatic fallback.

HandaaS的MCP服务器，提供全域资讯搜索、企业新闻、行业资讯、主题跟踪和企业动态监控能力，帮助用户开展行业研究和舆情分析。

An MCP server that implements Retrieval-Augmented Generation to efficiently retrieve and process important information from various sources, providing accurate and contextually relevant responses.

Minimal RAG-over-a-corpus MCP retrieval: search_knowledge returns cited chunks. Local embeddings.

MCP bridge to a multimodal RAG service, enabling hybrid search and Q&A over documents with tools for knowledge base queries and health checks.

A RAG-based knowledge base system supporting document processing, semantic search, and intelligent Q\&A with multiple AI model integrations.

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

A modular RAG server with hybrid search and LLM rerank, exposing 3 MCP tools for AI clients like Claude Desktop and Cursor.

A 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.

Enables agents to retrieve relevant context from local documents via MCP tools, supporting ingestion, semantic search, metadata filtering, and evidence inspection entirely on-device.

Local MCP server that provides a search_documents tool to query a RAG pipeline built with Ollama embeddings and ChromaDB, enabling Claude Desktop to retrieve relevant document chunks.

Hybrid RAG over Claude Code and Hermes session history via MCP tools for search, ingest, and context injection.

MCP server that enables semantic search over PDF documents using RAG with ChromaDB and OpenAI embeddings.

A CLI tool and MCP server that turns markdown documentation into a searchable, queryable knowledge base.

Enables conversational queries about a GitHub user's profile, using lazy indexing and tool calls to retrieve repository info and file contents, with cached results for efficiency.

MCP 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.

Enables semantic search over personal study notes by exposing a vector search tool that Claude Desktop can call to retrieve relevant note content and synthesize grounded answers.

Exposes hybrid retrieval (dense embeddings + BM25 + RRF) and document operations (search, fetch, rerank) as MCP tools, using Qdrant and OpenAI embeddings for local or server mode.

Enables broad web search for current, time-sensitive information, returning short title, URL, and snippet results rather than full pages. It supports region and result-count parameters through a keyless multi-engine backend.

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 a language model to query a document index through callable tools: ranked passage search, reading a chunk with its neighbours or a full document by id, index statistics, and sourced generated answers. The tools chain together, letting a model search, then read around the best chunk ids it received.

Enables local-first hybrid knowledge retrieval from authorized Markdown and plain-text files, combining full-text and vector search with reranking and traceable source references via a single search tool.

Gives an LLM client BM25 keyword search over a local corpus of Markdown and text files, letting it find ranked passages, fetch exact chunks with source offsets, read whole documents, and list what was indexed. It runs entirely locally over stdio with an in-memory index, calling no model and requiring no API key.

Enables semantic search over codebases using natural language queries, returning relevant code snippets with source locations. Integrates with Claude Code for automatic codebase exploration.

Exposes a RAG document-search API as MCP tools (rag_health, rag_ingest, rag_query), enabling agents to index and search markdown documents with cited results through natural language.