Mspaint Mcp Server V2

Mspaint MCP Server V2 – AI‑Driven Mathematical Problem Solving with Legacy UI Interaction

The Mspaint MCP Server V2 addresses a niche but powerful challenge: enabling large language models to solve multi‑step mathematical problems and then render the solution visually using a traditional Windows application. By bridging an LLM (Google’s Gemini) with MSPaint through the Model Context Protocol, developers can create assistants that not only compute complex expressions but also produce a clear, hand‑drawn representation of the answer. This capability is invaluable for educational tools, tutoring systems, and any workflow that benefits from a visual confirmation of calculations.

At its core, the server exposes a set of MCP tools implemented with . These tools provide programmatic control over MSPaint—opening the application, drawing shapes, and inserting text. The server also offers mathematical utilities such as arithmetic evaluation, verification, and consistency checks. An AI agent interacts with these tools via a structured prompting framework: the agent first reasons step‑by‑step, calls each tool in sequence, verifies intermediate results, and finally writes the answer onto a canvas before signalling completion. The strict single‑line response format ensures deterministic communication with the MCP client and prevents accidental loops or redundant calls.

Key features include:

• Structured Prompting Workflow – A templated prompt that enforces a disciplined reasoning and execution sequence, reducing hallucinations and ensuring every calculation is verified.
• Legacy UI Automation – Full control over MSPaint through , allowing the agent to draw rectangles, place text, and manage application state.
• Verification Tools – Built‑in functions to cross‑check calculations () and overall consistency (), giving developers confidence in the AI’s output.
• Single‑Line MCP Interaction – Responses are limited to one of three formats (function call, final answer, or completion signal), simplifying parsing and integration with existing MCP clients.

Typical use cases involve educational software where a student submits a math problem, the AI solves it step‑by‑step, and then paints the solution on a virtual whiteboard. It can also serve as a debugging aid for developers testing complex formulae, or as part of an automated report generator that produces both numeric results and illustrative graphics.

Integration is straightforward for developers familiar with MCP: expose the server’s tool set, provide the structured prompt to the LLM, and handle the single‑line responses. The server’s design emphasizes reliability—each tool call is verified, and the AI cannot proceed without confirming that MSPaint is correctly open and ready. This makes it a robust component in larger AI‑driven workflows that require both computation and visual output.