MSPaint MCP Server with AI-based Planning Algorithms
@shettysaish20
About MSPaint MCP Server with AI-based Planning Algorithms
Using Advanced AI Prompting to enhance LLM planning to solve complex math problems and draw the answer on MSPaint Canvas
Basic information
Config
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"MSPaint-MCP-Server-V2": {
"command": "python",
"args": [
"mcp_paint_app/mcp_client.py"
]
}
}
}Tools
No tools detected
We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.
Overview
What is MSPaint MCP Server with AI-based Planning Algorithms?
An MCP (Model Context Protocol) server that enables an AI agent powered by Google Gemini to automate legacy Windows MSPaint. The agent solves complex multi‑step math problems and draws the final answer on the Paint canvas using tools defined via fastmcp and pywinauto. It is intended for developers and researchers exploring structured prompting and tool‑augmented AI workflows.
How to use MSPaint MCP Server with AI-based Planning Algorithms?
- Create a Conda environment with Python 3.11 and activate it.
- Install dependencies with
pip install -r requirements.txt. - Place your Gemini API key in a
.envfile asGEMINI_API_KEY=YOUR_KEY. - Run the client:
python mcp_paint_app/mcp_client.py.
This starts the client, which connects to the MCP server, initializes the AI agent, and begins solving the provided math expression.
FAQ from MSPaint MCP Server with AI-based Planning Algorithms
What are the system dependencies and runtime requirements?
Python 3.11+ and Conda are recommended. Dependencies include pywin32, pywinauto, fastmcp, python-dotenv, google-genai, and rich. A Google Gemini API key is required.
What if the AI agent does not select the correct tools?
Review the system prompt in the client code; ensure tool descriptions are accurate and complete. Adjust the prompt if necessary.
How do I fix coordinate issues when drawing in MSPaint?
Coordinates used for clicking and drawing may need adjustment based on your screen resolution and window size. Use the debugging print statements in the code to identify and set the correct values.
What should I do if I encounter permission errors?
Try running
More Reasoning MCP servers
Node Code Sandbox MCP 🛠️
mozicim# 🐢🚀 Node.js Sandbox MCP ServerThis repository hosts a Node.js server that implements the Model Context Protocol (MCP) for running JavaScript in isolated Docker containers. It allows for on-the-fly npm dependency installation, making it easy to execute code safely and efficient
IntelliNode Medical Use Cases
BarqawizMulti-Agent AI Orchestration Workshop
🐢🚀 Node.js Sandbox MCP Server
alfonsograzianoA Node.js–based Model Context Protocol server that spins up disposable Docker containers to execute arbitrary JavaScript.
End-to-End Agentic AI Automation Lab
MDalamin5This repository contains hands-on projects, code examples, and deployment workflows. Explore multi-agent systems, LangChain, LangGraph, AutoGen, CrewAI, RAG, MCP, automation with n8n, and scalable agent deployment using Docker, AWS, and BentoML.
Agenticstore — The Open Source Standard For Local Mcp Tooling
agenticstoreAgenticStore: The secure toolkit for AI agents. Instantly equip Claude Desktop, Cursor, and Windsurf with 27+ MCP tools, persistent memory, and SearXNG search, all protected by a built-in PII prompt firewall to protect your data from being exposed to AI agents.
Comments