mcp-rag-server

mcp-rag-server

3.6
knowledge_and_memory

The mcp-rag-server is designed to facilitate Retrieval Augmented Generation (RAG) by indexing documents and serving relevant contexts via the Model Context Protocol (MCP). It features tools for document management and querying, supporting various embedding models for seamless integration with large language models.

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mcp-rag-server

npm version

A Model Context Protocol (MCP) server that enables Retrieval Augmented Generation (RAG). It indexes your documents and serves relevant context to Large Language Models via the MCP protocol.

Integration Examples

Generic MCP Client Configuration

{
  "mcpServers": {
    "rag": {
      "command": "npx",
      "args": ["-y", "mcp-rag-server"],
      "env": {
        "BASE_LLM_API": "http://localhost:11434/v1",
        "EMBEDDING_MODEL": "nomic-embed-text",
        "VECTOR_STORE_PATH": "./vector_store",
        "CHUNK_SIZE": "500"
      }
    }
  }
}

Example Interaction

# Index documents
>> tool:embedding_documents {"path":"./docs"}

# Check status
>> resource:embedding-status

<< rag://embedding/status
Current Path: ./docs/file1.md
Completed: 10
Failed: 0
Total chunks: 15
Failed Reason:

Table of Contents

Features

  • Index documents in .txt, .md, .json, .jsonl, and .csv formats
  • Customizable chunk size for splitting text
  • Local vector store powered by SQLite (via LangChain's LibSQLVectorStore)
  • Supports multiple embedding providers (OpenAI, Ollama, Granite, Nomic)
  • Exposes MCP tools and resources over stdio for seamless integration with MCP clients

Installation

From npm

npm install -g mcp-rag-server

From Source

git clone https://github.com/kwanLeeFrmVi/mcp-rag-server.git
cd mcp-rag-server
npm install
npm run build
npm start

Quick Start

export BASE_LLM_API=http://localhost:11434/v1
export EMBEDDING_MODEL=granite-embedding-278m-multilingual-Q6_K-1743674737397:latest
export VECTOR_STORE_PATH=./vector_store
export CHUNK_SIZE=500

# Run (global install)
mcp-rag-server

# Or via npx
npx mcp-rag-server

💡 Tip: We recommend using Ollama for embedding. Install and pull the nomic-embed-text model:

ollama pull nomic-embed-text
export EMBEDDING_MODEL=nomic-embed-text

Configuration

VariableDescriptionDefault
BASE_LLM_APIBase URL for embedding APIhttp://localhost:11434/v1
LLM_API_KEYAPI key for your LLM provider(empty)
EMBEDDING_MODELEmbedding model identifiernomic-embed-text
VECTOR_STORE_PATHDirectory for local vector store./vector_store
CHUNK_SIZECharacters per text chunk (number)500

💡 Recommendation: Use Ollama embedding models like nomic-embed-text for best performance.

Usage

MCP Tools

Once running, the server exposes these tools via MCP:

  • embedding_documents(path: string): Index documents under the given path
  • query_documents(query: string, k?: number): Retrieve top k chunks (default 15)
  • remove_document(path: string): Remove a specific document
  • remove_all_documents(confirm: boolean): Clear the entire index (confirm=true)
  • list_documents(): List all indexed document paths

MCP Resources

Clients can also read resources via URIs:

  • rag://documents — List all document URIs
  • rag://document/{path} — Fetch full content of a document
  • rag://query-document/{numberOfChunks}/{query} — Query documents as a resource
  • rag://embedding/status — Check current indexing status (completed, failed, total)

How RAG Works

  1. Indexing: Reads files, splits text into chunks based on CHUNK_SIZE, and queues them for embedding.
  2. Embedding: Processes each chunk sequentially against the embedding API, storing vectors in SQLite.
  3. Querying: Embeds the query and retrieves nearest text chunks from the vector store, returning them to the client.

Development

npm install
npm run build      # Compile TypeScript
npm start          # Run server
npm run watch      # Watch for changes

Contributing

Contributions are welcome! Please open issues or pull requests on GitHub.

License

MIT 2025 Quan Le