MCP RAG Server

Name: MCP RAG Server
Author: sylphlab

@sylphlab

代码仓库

关于 MCP RAG Server

暂无概览

#mathgpt #math-solver #math-assistant

基本信息

分类

记忆与知识

许可证

MIT license

运行时

node

传输方式

stdio

发布者

sylphlab

配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "rag-server-mcp-sylphlab": {
      "command": "docker",
      "args": [
        "exec",
        "ollama",
        "ollama",
        "pull",
        "nomic-embed-text"
      ]
    }
  }
}

工具

未检测到工具

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is MCP RAG Server?

MCP RAG Server is a Model Context Protocol (MCP) server that enables Retrieval Augmented Generation (RAG) for connected LLMs by indexing project documents and providing relevant context to enhance responses. It is built with Google Genkit, ChromaDB, and Ollama, and is designed for developers who want local, privacy‑focused RAG capabilities.

How to use MCP RAG Server?

The recommended setup uses Docker Compose: clone the repository, run docker-compose up -d --build, then pull the default embedding model with docker exec ollama ollama pull nomic-embed-text. Configure environment variables (e.g., CHROMA_URL, OLLAMA_HOST, INDEX_PROJECT_ON_STARTUP) in the docker-compose.yml file. The server exposes five MCP tools (indexDocuments, queryDocuments, removeDocument, removeAllDocuments, listDocuments) for integration with any MCP client.

Key features of MCP RAG Server

Automatic indexing of project files on startup (configurable)
Supports .txt, .md, code files, .json, .jsonl, .csv
Hierarchical chunking for Markdown (text and code blocks separated)
Uses ChromaDB for persistent vector storage
Local embeddings via Ollama (default: nomic-embed-text)
Exposes all RAG functions as standard MCP tools

Use cases of MCP RAG Server

Enhance LLM responses with context from local project documentation and code
Build a fully local, privacy‑preserving document Q&A system
Automatically index source files to improve code‑assistance tools
Combine MCP with RAG for domain‑specific knowledge retrieval

FAQ from MCP RAG Server

What are the runtime dependencies of MCP RAG Server?

The server requires ChromaDB (vector store), Ollama (local embeddings), and Google Genkit. All are containerized when using Docker Compose.

How are documents indexed?

By default the server scans the project directory on startup and indexes all supported files. Automatic indexing can be disabled with the INDEX_PROJECT_ON_STARTUP environment variable, and manual indexing is available via the indexDocuments tool.

What file types does MCP RAG Server support?

It supports .txt, .md, code files (basic chunking), .json, .jsonl, and .csv.

How do I start MCP RAG Server?

The recommended method is Docker Compose: clone the repo, run docker-compose up -d --build, then pull the embedding model. For local development without Docker, ensure ChromaDB and Ollama are running, set the required environment variables, and run npm start.

Where are the embeddings and indexed data stored?

ChromaDB provides persistent vector storage. In the Docker Compose setup, the data is stored inside Docker volumes, ensuring it survives container restarts.

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