Agent MCP Math Draw

🚀 Math Drawing Client Based on MCP

talk2mcp_math_draw_client.py is a Python application that combines mathematical computation and visual drawing functions. It utilizes the Gemini AI model to handle mathematical problems and generate corresponding responses.

🚀 Quick Start

This application integrates mathematical computation and visualization, leveraging the Gemini AI model to solve mathematical problems and present results visually.

✨ Features

Implemented Features

• Integration with Gemini AI for solving mathematical problems
• Drawing function to visually display mathematical results
• Iterative problem-solving approach (up to 9 iterations)
• Support for basic mathematical operations and ASCII value calculations
• Automatic result plotting on the canvas
• Basic error handling and recovery mechanisms
• Simple canvas operations
• Basic text placement function
• Operation timeout control (default 10-second timeout for LLM operations)
• Session management
• Tool execution and parameter validation

📦 Installation

Server Requirements

1. MCP Server Setup:

   • Python 3.x runtime environment
   • Installed MCP server package
   • Necessary server dependencies:
     • mcp package
     • google-generativeai for AI integration
     • python-dotenv for environment management

2. Server Configuration:

   • Port settings (default: 8080)
   • Memory allocation for mathematical operations
   • Canvas size settings (default: 1920x1080)
   • Maximum concurrent connections
   • Timeout control (default: 10 seconds)

3. Prerequisites:

   • Install necessary Python packages
   • Configure the Gemini API key
   • Ensure network connectivity

Environment Setup

1. Create a config.json file with the following content:

   {
     "canvas_size": "1920x1080",
     "max_connections": 5,
     "timeout": 10
   }
   

   Place it in the project root directory.

📚 Documentation

Function Implementation

• Connect to the MCP server using standard input-output communication
• Use the Gemini AI model to handle mathematical queries
• Automatically display results on the canvas
• Solve complex problems with up to 9 iterations
• Validate operations through the tool mode
• Built-in error handling mechanism

Error Handling

The system includes the following error handling:

• API timeout (default 10 seconds)
• Invalid input
• Connection issues
• Tool execution errors
• Drawing tool failures
• Parameter validation errors
• Session management problems

Performance Tips

1. Optimization Suggestions:

   • Use an appropriate canvas size
   • Limit the number of concurrent operations
   • Clean up unused resources
   • Monitor memory usage
   • Use efficient algorithms

2. Best Practices:

   • Regularly back up the system
   • Update software versions
   • Monitor system resources
   • Log error messages
   • Conduct performance tests

Troubleshooting

1. Common Issues:

   • Connection timeout
   • Drawing tool failures
   • Insufficient memory
   • Poor performance
   • Authentication errors

2. Solutions:

   • Check the server status
   • Ensure network connectivity
   • Clean up cache files
   • Update dependencies
   • Monitor system resource usage