Groq MCP

🚀 VSCodeMCP

TODO: Retire this or use it only as a VSCODE script. If the GROQ-MCP-SERVER doesn't work with VSCODE COPILOT in the box, we need to figure out what the problem is.

A Python module for creating and managing an MCP (Model Context Protocol) server for Visual Studio Code's Copilot feature, with optional integration of Groq LLM.

🚀 Quick Start

VSCodeMCP module simplifies the following tasks:

1. Define MCP tools, resources, and prompts for VS Code's Copilot agent functionality.
2. Automatically generate VS Code configuration files.
3. Install the server using VS Code's command-line interface.
4. Create workspace sharing configuration files.
5. Integrate with Groq LLM for powerful AI-driven tools.

✨ Features

• 🛠️ Simple Tool Definition: Define MCP tools using simple Python functions.
• 📊 VS Code Configuration: Automatically generate VS Code configuration files.
• 🔗 FastMCP Integration: Integration with FastMCP.
• Performance Optimization: Efficient model inference speed.
• Cost-Effectiveness: Lower per-token cost.
• Model Diversity: Access models specialized for different tasks.
• Local Control: Direct integration without third-party dependencies.
• Customization: Fine-tune the behavior of each tool through custom system prompts.

📦 Installation

Requirements

• Python 3.7+
• fastmcp library
• Visual Studio Code with Copilot
• groq library (optional, for Groq integration)
• Groq API key (for Groq integration)

📚 Documentation

Project Structure

• vscode_mcp.py - The core module for VS Code MCP integration.
• groq_vscode_mcp.py - An extension module that adds Groq LLM support.
• examples/ - Example MCP servers
  • sample_mcp_server.py - A basic example of an MCP server.
  • advanced_mcp_server.py - An advanced example with custom tools.
  • example_groq_mcp_server.py - An example using Groq LLM.

Why Integrate with Groq?

• High Performance: Groq's infrastructure provides extremely fast inference speeds.
• Cost Efficiency: It may have a lower per-token cost than other LLM providers.
• Model Diversity: Access models specialized for different tasks.
• Local Control: Direct integration without third-party dependencies.
• Customization: Fine-tune the behavior of each tool through custom system prompts.

📄 License

This project is licensed under the MIT License.

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