RagDocs MCP Server

A Model Context Protocol (MCP) server that provides RAG (Retrieval-Augmented Generation) capabilities using Qdrant vector database and Ollama/OpenAI embeddings. This server enables semantic search and management of documentation through vector similarity.

Features

• Add documentation with metadata

• Semantic search through documents

• List and organize documentation

• Delete documents

• Support for both Ollama (free) and OpenAI (paid) embeddings

• Automatic text chunking and embedding generation

• Vector storage with Qdrant

Related MCP server: Qdrant Retrieve MCP Server

Prerequisites

• Node.js 16 or higher

• One of the following Qdrant setups:

  • Local instance using Docker (free)

  • Qdrant Cloud account with API key (managed service)

• One of the following for embeddings:

  • Ollama running locally (default, free)

  • OpenAI API key (optional, paid)

Available Tools

1. add_document

Add a document to the RAG system.

Parameters:

• url (required): Document URL/identifier

• content (required): Document content

• metadata (optional): Document metadata

  • title: Document title

  • contentType: Content type (e.g., "text/markdown")

2. search_documents

Search through stored documents using semantic similarity.

Parameters:

• query (required): Natural language search query

• options (optional):

  • limit: Maximum number of results (1-20, default: 5)

  • scoreThreshold: Minimum similarity score (0-1, default: 0.7)

  • filters:

    • domain: Filter by domain

    • hasCode: Filter for documents containing code

    • after: Filter for documents after date (ISO format)

    • before: Filter for documents before date (ISO format)

3. list_documents

List all stored documents with pagination and grouping options.

Parameters (all optional):

• page: Page number (default: 1)

• pageSize: Number of documents per page (1-100, default: 20)

• groupByDomain: Group documents by domain (default: false)

• sortBy: Sort field ("timestamp", "title", or "domain")

• sortOrder: Sort order ("asc" or "desc")

4. delete_document

Delete a document from the RAG system.

Parameters:

• url (required): URL of the document to delete

Installation

npm install -g @mcpservers/ragdocs

MCP Server Configuration

{
  "mcpServers": {
    "ragdocs": {
      "command": "node",
      "args": ["@mcpservers/ragdocs"],
      "env": {
        "QDRANT_URL": "http://127.0.0.1:6333",
        "EMBEDDING_PROVIDER": "ollama"
      }
    }
  }
}

Using Qdrant Cloud:

{
  "mcpServers": {
    "ragdocs": {
      "command": "node",
      "args": ["@mcpservers/ragdocs"],
      "env": {
        "QDRANT_URL": "https://your-cluster-url.qdrant.tech",
        "QDRANT_API_KEY": "your-qdrant-api-key",
        "EMBEDDING_PROVIDER": "ollama"
      }
    }
  }
}

Using OpenAI:

{
  "mcpServers": {
    "ragdocs": {
      "command": "node",
      "args": ["@mcpservers/ragdocs"],
      "env": {
        "QDRANT_URL": "http://127.0.0.1:6333",
        "EMBEDDING_PROVIDER": "openai",
        "OPENAI_API_KEY": "your-api-key"
      }
    }
  }
}

Local Qdrant with Docker

docker run -d --name qdrant -p 6333:6333 -p 6334:6334 qdrant/qdrant

Environment Variables

• QDRANT_URL: URL of your Qdrant instance

  • For local: "http://127.0.0.1:6333" (default)

  • For cloud: "https://your-cluster-url.qdrant.tech"

• QDRANT_API_KEY: API key for Qdrant Cloud (required when using cloud instance)

• EMBEDDING_PROVIDER: Choice of embedding provider ("ollama" or "openai", default: "ollama")

• OPENAI_API_KEY: OpenAI API key (required if using OpenAI)

• EMBEDDING_MODEL: Model to use for embeddings

  • For Ollama: defaults to "nomic-embed-text"

  • For OpenAI: defaults to "text-embedding-3-small"

License

Apache License 2.0