RAG MCP Servers

Find MCP servers for retrieval-augmented generation workflows with vector search, embeddings, knowledge bases, and source-grounded context.

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Matching MCP servers

Pulled from the existing MCP Servers directory with no separate topic database.

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RAG Documentation MCP Server

Retrieve and process documentation using vector search to provide relevant context for AI assistants.

analyze-coverage-mcp

MCP server that bridges LCOV coverage reports to AI agents.

Baidu iRAG MCP Server

Generate images using Baidu's iRAG API through a standardized MCP interface.

An MCP server for brokerage functionalities, built with the MCP framework.

Code Graph RAG MCP

Code Rag with Graph - local only installation

Gemini CLI RAG MCP

A RAG-based Q&A server using a vector store built from Gemini CLI documentation.

IBM Storage Insights MCP Server

An open-source MCP server providing real-time observability for IBM Storage Insights assets.

Qdrant RAG MCP Server

A semantic search server for codebases using Qdrant, featuring intelligent GitHub issue and project management.

Embedding MCP Server

An MCP server powered by txtai for semantic search, knowledge graphs, and AI-driven text processing.

gemini-embedding-2-mcp

A powerful Model Context Protocol (MCP) server using gemini embedding 3 that transforms any local directory into an ultrafast, visually-aware spatial search engine for AI agents.

better-code-review-graph

Knowledge graph for token-efficient code reviews with Tree-sitter parsing, dual-mode embedding (ONNX + LiteLLM), and blast-radius analysis via MCP tools.

Zero-dependency persistent AI memory using SQLite. Dual-store, pluggable embeddings, 10 MCP tools.

Where RAG MCP fits

Give agents a retrieval layer for source-grounded answers, coding context, support knowledge, and research workflows.

Connect vector databases, document stores, embeddings, and search APIs through MCP instead of one-off prompt uploads.

Build repeatable RAG stacks where agents can retrieve, cite, and inspect context before generating output.

Setup checklist

1Choose a RAG server based on your storage layer, embedding workflow, and retrieval controls.
2Create read-only credentials for the relevant vector database, docs source, or knowledge base.
3Add the server command or remote endpoint to your MCP client configuration.
4Test a known query and confirm the agent receives source URLs, snippets, metadata, or citation handles.

How to choose

Prefer servers that expose source-aware results with scores, metadata, and collection or namespace controls.
Check whether the server handles ingestion, retrieval only, or both.
Use separate configurations for private knowledge, public docs, and experimental embeddings.

RAG MCP FAQ

What is RAG MCP?

RAG MCP connects an AI client to retrieval tools so agents can search knowledge bases, vector stores, and documents before answering or taking action.

How is RAG MCP different from Knowledge Retrieval MCP?

Knowledge retrieval is the broader workflow. RAG MCP is more focused on retrieval-augmented generation with embeddings, vector search, ranking, and source-grounded context.

Do I need a vector database for RAG MCP?

Not always. Some workflows use search APIs, document stores, or managed knowledge bases. Vector databases are useful when semantic retrieval and custom collections matter.