MCP RAG
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 in
What is MCP RAG?
MCP RAG is a Model Context Protocol (MCP) server that allows AI assistants like Claude, Cursor, and VS Code to 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 docum...
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 in
This server falls under the Knowledge & Memory and Data Science & ML categories on MCPgee, the world's largest MCP server directory with 33,000+ servers.
Features
- mcp-rag-server is a Model Context Protocol (MCP) server that
Use Cases
Maintainer
Works with
Installation
Manual Installation
npx mcp-ragConfiguration
Configuration Details
claude_desktop_config.json
Performance
Response Metrics
Resource Usage
How to Set Up and Use MCP RAG
MCP-RAG is a Model Context Protocol server that adds Retrieval Augmented Generation (RAG) capabilities to any MCP-compatible AI assistant. It allows you to index your own document collections and then have the AI retrieve and reason over relevant passages when answering questions, rather than relying solely on its training data. This is particularly useful for grounding AI responses in proprietary documentation, research papers, codebases, or any corpus of text that the model has not seen during training.
Prerequisites
- Node.js 18 or higher installed
- npm installed
- Document files you want to index (text, markdown, PDF, or similar formats)
- Claude Desktop or another MCP-compatible client
Install the mcp-rag package
Run the server directly using npx without a global install, or install it globally.
# Run without installing
npx mcp-rag
# Or install globally
npm install -g mcp-ragPrepare your document collection
Gather the documents you want to make searchable. Place them in a directory the server can access. Common formats include plain text, Markdown, and PDF files.
Configure your MCP client
Add the mcp-rag server to your claude_desktop_config.json. The server communicates over stdio transport.
Index your documents
Once the server is running and connected to your AI client, use the indexing tool to load your documents into the RAG index. The server will chunk and embed the content for retrieval.
Query your documents through the AI
Ask questions about the content you indexed. The server retrieves the most relevant passages and provides them to the AI as context for its response.
MCP RAG Examples
Client configuration
Add mcp-rag to your claude_desktop_config.json to enable RAG capabilities in Claude Desktop.
{
"mcpServers": {
"mcp-rag": {
"command": "npx",
"args": ["mcp-rag"]
}
}
}Prompts to try
Use these prompts once you have indexed documents to exercise retrieval and question-answering.
- "Index the documents in /Users/me/docs/project-specs"
- "What does our API documentation say about authentication?"
- "Search my documents for information about the deployment process"
- "Find passages in the indexed documents related to error handling"
- "Summarize what the indexed documents say about the data model"Troubleshooting MCP RAG
npx mcp-rag fails with package not found
The package may not be published under this exact name. Check the GitHub repository at https://github.com/seanshin0214/mcp-rag for the current install instructions, as the repository may have been renamed or made private.
Documents are indexed but retrieval returns irrelevant results
RAG quality depends on chunk size and embedding quality. Try re-indexing with smaller document chunks if the server supports chunking configuration, and ensure your documents are in a clean text format without heavy formatting noise.
Server connects but no tools appear in the AI client
Restart the MCP client after adding the config entry. If tools still do not appear, run the server manually in a terminal ('npx mcp-rag') to check for startup errors before adding it to the client config.
Frequently Asked Questions about MCP RAG
What is MCP RAG?
MCP RAG is a Model Context Protocol (MCP) server that 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 in It connects AI assistants to external tools and data sources through a standardized interface.
How do I install MCP RAG?
Follow the installation instructions on the MCP RAG GitHub repository. Clone the repo, install dependencies, and add the server config to your AI client.
Which AI clients work with MCP RAG?
MCP RAG works with all major MCP-compatible AI clients including Claude Desktop, Claude Code, Cursor, VS Code (GitHub Copilot), Windsurf, and Cline.
Is MCP RAG free to use?
Yes, MCP RAG is open source and available under the MIT license. You can use it freely in both personal and commercial projects.
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Quick Config Preview
Add this to your claude_desktop_config.json or .cursor/mcp.json
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