Ragdocs

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

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”