RagDocs MCP Server: AI-Powered Document Search & Workflow Optimization

Use Cases of RagDocs MCP Server: AI-Powered Document Search & Workflow Optimization?

Teams in technical writing, customer support, and research benefit from RagDocs’ capabilities:

• Enterprise Documentation:** Centralize technical manuals and product specs for fast internal queries.
• Developer Ecosystems:** Maintain up-to-date API references with real-time semantic search.
• Customer Support:** Analyze and resolve queries using ticket history and knowledge bases.
• Academic Research:** Accelerate literature reviews by linking papers through contextual links.

Q: How do I choose between Ollama and OpenAI?

A: Use Ollama for open-source efficiency in development environments. Opt for OpenAI when enterprise-grade accuracy or proprietary models are required.

Q: Can I run Qdrant locally?

A: Yes, deploy via Docker for lightweight local testing or scale to cloud instances for production use.

Q: What document formats are supported?

A: Supports plain text, PDF, and markdown natively. Extend via custom preprocessors for specialized formats.

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"

License

Apache License 2.0