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Qdrant MCP Server

Qdrant MCP Server

Local
github.com/hadvDatabases4Updated 73w ago

About Qdrant MCP Server

WisdomForge is a knowledge management and retrieval system built around vector databases. It is for storing and retrieving domain knowledge, supports Qdrant or Chroma, and uses Qdrant's built-in FastEmbed for embedding generation.

Key features

  1. 01Manage and retrieve knowledge
  2. 02Support multiple knowledge types
  3. 03Select database through environment variables
  4. 04Generate embeddings with FastEmbed
  5. 05Deploy to Smithery.ai

Use cases

  1. 01Store domain knowledge
  2. 02Retrieve lessons and best practices
  3. 03Switch vector databases through configuration

MCP support

This RAG-oriented MCP server exposes knowledge storage and retrieval to MCP clients. It can be configured with Qdrant or Chroma and supports HTTP server mode.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

WisdomForge

A powerful knowledge management system that forges wisdom from experiences, insights, and best practices. Built with Qdrant vector database for efficient knowledge storage and retrieval.

Features

  • Intelligent knowledge management and retrieval
  • Support for multiple knowledge types (best practices, lessons learned, insights, experiences)
  • Configurable database selection via environment variables
  • Uses Qdrant's built-in FastEmbed for efficient embedding generation
  • Domain knowledge storage and retrieval
  • Deployable to Smithery.ai platform

Prerequisites

  • Node.js 20.x or later (LTS recommended)
  • npm 10.x or later
  • Qdrant or Chroma vector database

Installation

  1. Clone the repository:
git clone https://github.com/hadv/wisdomforge
cd wisdomforge
  1. Install dependencies:
npm install
  1. Create a .env file in the root directory based on the .env.example template:
cp .env.example .env
  1. Configure your environment variables in the .env file:
Required Environment Variables
Database Configuration
  • DATABASE_TYPE: Choose your vector database (qdrant or chroma)
  • COLLECTION_NAME: Name of your vector collection
  • QDRANT_URL: URL of your Qdrant instance (required if using Qdrant)
  • QDRANT_API_KEY: API key for Qdrant (required if using Qdrant)
  • CHROMA_URL: URL of your Chroma instance (required if using Chroma)
Server Configuration
  • HTTP_SERVER: Set to true to enable HTTP server mode
  • PORT: Port number for local development only (default: 3000). Not used in Smithery cloud deployment.

Example .env configuration for Qdrant:

DATABASE_TYPE=qdrant
COLLECTION_NAME=wisdom_collection
QDRANT_URL=https://your-qdrant-instance.example.com:6333
QDRANT_API_KEY=your_api_key
HTTP_SERVER=true
PORT=3000  # Only needed for local development
  1. Build the project:
npm run build

AI IDE Integration

Cursor AI IDE

Add this configuration to your ~/.cursor/mcp.json or .cursor/mcp.json file:

{
  "mcpServers": { 
    "wisdomforge": {
      "command": "/bin/zsh",
      "args": [
        "/path/to/wisdomforge/run-wisdomforge-mcp.sh"
      ]
    }
  }
}

Replace the following placeholders in the configuration:

  • YOUR_API_KEY: Your Smithery API key
  • YOUR_COLLECTION_NAME: Your Qdrant collection name
  • YOUR_QDRANT_URL: Your Qdrant instance URL
  • YOUR_QDRANT_API_KEY: Your Qdrant API key

Note: Make sure you have Node.js installed and npx available in your PATH. If you're using nvm, ensure you're using the correct Node.js version by running nvm use --lts before starting Cursor.

Claude Desktop

Add this configuration in Claude's settings:

{
  "processes": {
    "knowledge_server": {
      "command": "/path/to/your/project/run-mcp.sh",
      "args": []
    }
  },
  "tools": [
    {
      "name": "store_knowledge",
      "description": "Store domain-specific knowledge in a vector database",
      "provider": "process",
      "process": "knowledge_server"
    },
    {
      "name": "retrieve_knowledge_context",
      "description": "Retrieve relevant domain knowledge from a vector database",
      "provider": "process",
      "process": "knowledge_server"
    }
  ]
}

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