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RAG MCP Server（RAGDB)

@musoukun

Repository

Basic information

Category

Databases

Transports

stdio

Author

musoukun

About RAG MCP Server（RAGDB)

MCP対応のRAGシステム。Markdownドキュメントをベクトル化し、自然言語で高速検索。LibSQL、Qdrant、PostgreSQLに対応してます。

#ragdb#mcp#search-assistant

RAG MCP Server（RAGDB) config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "rag-server": {
      "command": "node",
      "args": [
        "your-ragdb-mcp-path/rag-mcp/dist/index.js"
      ],
      "env": {
        "RAG_DATABASE_TYPE": "libsql",
        "RAG_CONNECTION_URL": "file:your-ragdb-mcp-path/rag-mcp/libsql/rag.db",
        "EMBEDDING_PROVIDER": "google",
        "EMBEDDING_MODEL": "text-embedding-004",
        "EMBEDDING_API_KEY": "your-google-api-key",
        "EMBEDDING_DIMENSIONS": "768",
        "RAG_CHUNK_SIZE": "512",
        "RAG_CHUNK_OVERLAP": "50",
        "RAG_TOP_K": "5",
        "RAG_STRATEGY": "markdown",
        "AUTO_CREATE_INDEXES": "documents,technical",
        "LOG_LEVEL": "info"
      }
    }
  }
}

RAG MCP Server（RAGDB) tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is RAG MCP Server（RAGDB）?

RAG MCP Server（RAGDB） is an MCP server that allows MCP-compatible chat applications to manage and search a RAGDB. It vectorizes Markdown documents, stores them in a vector database, and enables retrieval through natural language questions or semantic similarity.

How to use RAG MCP Server（RAGDB）?

Install by adding a JSON configuration block to your MCP client settings (e.g., VS Code User Settings JSON or Claude Desktop). Set required environment variables for the database type, connection URL, embedding provider, and API key. Then invoke tools such as create_index, add_document, search_documents, or rag_search directly from the chat interface.

Key features of RAG MCP Server（RAGDB）

  • Save Markdown documents as vector embeddings in RAGDB.
  • Search documents using natural language questions or semantic similarity.
  • Choose from three databases: LibSQL (local), Qdrant (cloud), or PostgreSQL.
  • Batch register multiple Markdown files at once.
  • Supports OpenAI and Google embedding models.
  • Configurable chunk size, overlap, and number of search results.

Use cases of RAG MCP Server（RAGDB）

  • Store internal technical documentation and retrieve it via chat.
  • Ask questions about project documents during development.
  • Build a personal knowledge base that can be queried conversationally.
  • Manage and update document collections with versioning support.

Frequently asked questions

1

What databases does RAG MCP Server（RAGDB） support?

It supports LibSQL (local file-based, no DB server needed), Qdrant (cloud-ready for large data), and PostgreSQL (tested, for large data). For Qdrant and PostgreSQL, a database server is required.

2

What embedding providers are supported?

Two providers are supported: OpenAI and Google. You must provide the corresponding API key and set the embedding model name and dimensions via environment variables.

3

Can I use this server commercially?

Yes, free use, modification, and commercial use are allowed. However, the implementation uses Mastra libraries (licensed under Elastic License 2.0), so directly offering the Mastra Playground as a SaaS service is not permitted.

4

How do I configure the server for my environment?

Add a JSON block to your MCP client settings with the `mcpServers` entry. Set at least `RAG_DATABASE_TYPE`, `RAG_CONNECTION_URL`, `EMBEDDING_PROVIDER`, and `EMBEDDING_API_KEY`. Optional settings include chunk size, overlap, and auto-create index names.

5

What tools does RAG MCP Server（RAGDB） provide?

It offers tools such as `create_index`, `delete_index`, `add_document`, `update_document`, `search_documents`, `rag_search`, `advanced_rag_search`, `semantic_similarity_search`, `list_documents`, and `get_rag_info`. Each tool has specific parameters documented in the README.

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