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
- 01Manage and retrieve knowledge
- 02Support multiple knowledge types
- 03Select database through environment variables
- 04Generate embeddings with FastEmbed
- 05Deploy to Smithery.ai
Use cases
- 01Store domain knowledge
- 02Retrieve lessons and best practices
- 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.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEWisdomForge
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
- Clone the repository:
git clone https://github.com/hadv/wisdomforge
cd wisdomforge
- Install dependencies:
npm install
- Create a
.envfile in the root directory based on the.env.exampletemplate:
cp .env.example .env
- Configure your environment variables in the
.envfile:
Required Environment Variables
Database Configuration
DATABASE_TYPE: Choose your vector database (qdrantorchroma)COLLECTION_NAME: Name of your vector collectionQDRANT_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 totrueto enable HTTP server modePORT: 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
- 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 keyYOUR_COLLECTION_NAME: Your Qdrant collection nameYOUR_QDRANT_URL: Your Qdrant instance URLYOUR_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"
}
]
}
Comments
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