IBHack MCP Server

A Model Context Protocol (MCP) server that provides intelligent tool discovery and recommendation capabilities using Google Gemini AI. The server can scan Python directories for tool classes and recommend the most relevant tools based on user queries.

Features

• Tool Discovery: Automatically scans Python directories to discover tool classes

• AI-Powered Recommendations: Uses Google Gemini to recommend the most relevant tools based on user queries

• HTTP Transport: Runs as an HTTP server for easy integration

• Complete Code Generation: Returns full Python code for recommended tools

Related MCP server: Hermes Atlas MCP Server

Environment Variables

The following environment variables need to be set to run the MCP server:

Required

• GEMINI_API_KEY: Your Google AI API key for Gemini integration

  • Example: export GEMINI_API_KEY="your-api-key-here"

Optional

• SCAN_DIRECTORY: Directory path to scan for tools during startup

  • Example: export SCAN_DIRECTORY="/path/to/your/tools"

Installation

1. Install Poetry (if not already installed):

   curl -sSL https://install.python-poetry.org | python3 -

2. Install dependencies:

   poetry install

3. Set environment variables:

   export GEMINI_API_KEY="your-api-key-here"
   export SCAN_DIRECTORY="/path/to/your/tools"  # Optional

Running the Server

Method 1: Direct Python execution

python server.py

Method 2: Using Poetry

poetry run python server.py

Method 3: Using the Poetry script

poetry run mcp-server

The server will start on http://127.0.0.1:8000/mcp by default.

Configuration

Server Configuration

The server runs with the following default settings:

• Host: 127.0.0.1

• Port: 8000

• Path: /mcp

• Transport: HTTP

To modify these settings, edit the mcp.run() call in server.py:

mcp.run(transport="http", host="127.0.0.1", port=8000, path="/mcp")

Tool Discovery

The server looks for Python classes that:

• Have methods: get_name(), get_description(), and execute()

• Are located in .py files within the specified directory

• Can optionally have get_input_schema() and get_output_schema() methods

Available Tools

recommend_tools

Finds the most relevant tools for a given description using Gemini AI.

Parameters:

• query_description (string): Description of what the user wants to do

• top_k (integer, optional): Number of top tools to return (default: 2)

Returns:

• Dictionary containing recommended tools with complete Python code

• Success status and error information

• Tool metadata (name, description, file path, class name)

Example Usage

Using the Client

import asyncio
from client import MCPClientInterface

async def main():
    client = MCPClientInterface("http://127.0.0.1:8000/mcp")
    
    # Connect to server
    await client.connect()
    
    # Get tool recommendations
    result = await client.execute_tool(
        "recommend_tools",
        query_description="I need to process CSV data",
        top_k=3
    )
    
    print(result)
    
    await client.disconnect()

asyncio.run(main())

Direct HTTP Request

curl -X POST http://127.0.0.1:8000/mcp \
  -H "Content-Type: application/json" \
  -d '{
    "jsonrpc": "2.0",
    "id": 1,
    "method": "tools/call",
    "params": {
      "name": "recommend_tools",
      "arguments": {
        "query_description": "I need to analyze data",
        "top_k": 2
      }
    }
  }'