MCP MSPaint App Automation

@shettysaish20

代码仓库

基本信息

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生产力

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stdio

作者

shettysaish20

关于 MCP MSPaint App Automation

A simple Model Context Protocol (MCP) server client code to solve math problems and show the solution in MSPaint application

#mathgpt #math-solver #math-assistant

MCP MSPaint App Automation 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "MSPaint-MCP-Server": {
      "command": "python",
      "args": [
        "mcp_paint_app/mcp_client.py"
      ]
    }
  }
}

MCP MSPaint App Automation 工具

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工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is Model Context Protocol (MCP) MSPaint App Automation?

This project automates interactions with the legacy Windows application MSPaint using the Model Context Protocol (MCP). It leverages pywinauto to control Paint and fastmcp to define tools that an AI agent, powered by Google's Gemini model, can call via natural language instructions.

How to use Model Context Protocol (MCP) MSPaint App Automation?

Set up a Python 3.11 Conda environment, install dependencies from requirements.txt, and add your Gemini API key to a .env file. Then run the MCP client with python mcp_paint_app/mcp_client.py.

Key features of Model Context Protocol (MCP) MSPaint App Automation

Uses pywinauto to control the MSPaint application.
Exposes Paint automation functions as MCP tools via fastmcp.
AI agent driven by Google Gemini model.
Tools include open_paint, draw_rectangle, and add_text_in_paint.
Accepts natural language instructions to trigger paint actions.

Use cases of Model Context Protocol (MCP) MSPaint App Automation

Automate drawing shapes and adding text in MSPaint via an AI agent.
Demonstrate how MCP can control legacy Windows applications.
Enable generative AI to perform visual output through Paint.
Serve as a template for integrating AI agents with desktop automation.

常见问题

What is the Model Context Protocol (MCP)?

MCP is a framework that enables AI models to interact with external tools and resources in a standardized way.

What are the system requirements?

Python 3.11+, Conda (recommended), a Google Gemini API key, and packages: `pywin32`, `pywinauto`, `fastmcp`, `python-dotenv`, and `google-genai`.

How do I set up the Gemini API key?

Create a `.env` file in the project directory and add a line `GEMINI_API_KEY=YOUR_API_KEY`. The key is then loaded by `python-dotenv`.

What should I do if the coordinates are off?

Adjust the click coordinates in the code based on your screen resolution and window size. Use the provided debugging print statements to identify correct values.

What if the AI agent selects the wrong tool?

Review the system prompt and ensure the tool descriptions in the MCP server are accurate and specific enough for the agent to choose correctly.

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