What is Vito Mcp

vito-mcp is a Model Context Protocol (MCP) server implementation designed for Retrieval-Augmented Generation (RAG), facilitating intelligent knowledge management and retrieval.

Use cases

Use cases for vito-mcp include building knowledge bases, enhancing AI-driven applications with contextual information, and managing organizational knowledge effectively.

How to use

To use vito-mcp, clone the repository, install dependencies, configure the environment variables in a .env file, and then build and run the server. Ensure you have the required Node.js and vector database setup.

Key features

Key features of vito-mcp include intelligent knowledge management, support for various knowledge types, configurable database selection, efficient embedding generation using Qdrant’s FastEmbed, and deployability to the Smithery.ai platform.

Where to use

vito-mcp can be used in knowledge management systems, AI applications, and any domain requiring efficient retrieval of insights, best practices, and experiences.

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

2. Install dependencies:

npm install

3. Create a .env file in the root directory based on the .env.example template:

cp .env.example .env

4. 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

5. 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"
    }
  ]
}