MATLAB MCP Server

MATLAB MCP Tool – Interactive MATLAB Development for AI Assistants

The MATLAB MCP Tool is a dedicated Model Context Protocol server that bridges AI assistants with the MATLAB computational engine. It addresses the common pain point of enabling dynamic, stateful MATLAB execution from within conversational AI workflows. By exposing a suite of high‑level tools—such as script and section execution, workspace inspection, and script creation—the server lets developers treat MATLAB as a first‑class collaborator in code generation, debugging, and data analysis tasks.

Why this server matters.
AI assistants like Claude often generate code that must be executed to validate logic or produce visual results. When the target language is MATLAB, a specialized runtime environment is required to preserve workspace state across calls and to render plots inline. The MATLAB MCP Tool encapsulates the MATLAB Python engine, handling all inter‑process communication and workspace persistence. This removes boilerplate setup from the developer’s side, allowing the assistant to focus on high‑level reasoning while the server manages MATLAB’s execution context.

Key capabilities in plain language.

• Full script execution – Run an entire file or a block of code, with the server returning outputs, variable values, and any generated figures.
• Section‑based execution – Execute individual cell sections delimited by , enabling fine‑grained control and incremental development.
• Workspace introspection – Query the current MATLAB workspace for variable listings, types, and values, keeping the AI informed of state changes.
• Script creation – Generate new MATLAB script files from prompts, facilitating rapid prototyping within the assistant’s workflow.
• Plot capture – Automatically capture and transmit MATLAB figures, allowing visual feedback directly in the chat interface.

Real‑world use cases.
A data scientist can ask an AI assistant to build a model in MATLAB, then have the server run the script and return plots of training loss or confusion matrices. A teaching assistant can generate example MATLAB code for a lecture, execute it on demand, and display the resulting figures to students. Engineers integrating MATLAB simulations into larger pipelines can use section execution to step through simulation stages without restarting the engine each time.

Integration with AI workflows.
The server is configured via a simple JSON snippet that can be dropped into an MCP‑compatible client such as Cursor or Claude Code. Once started, the assistant can invoke any of the exposed tools by name, passing JSON arguments that mirror MATLAB syntax. The server handles all communication over the MCP protocol, ensuring that tool calls are authenticated, auto‑approved where appropriate, and return results in a format the assistant can embed directly into responses.

Unique advantages.
Unlike generic code execution services, this MCP server preserves MATLAB’s rich workspace across calls, meaning variables defined in one step are immediately available for subsequent steps. It also natively captures graphical output, a feature often missing in text‑only execution backends. The auto‑detection installer (not covered here) further reduces friction by locating MATLAB installations and configuring the Python environment, making it possible to start a seamless AI‑MATLAB loop in minutes.

In summary, the MATLAB MCP Tool empowers developers to weave MATLAB’s powerful numerical and visualization capabilities into conversational AI workflows, delivering an interactive, stateful experience that accelerates prototyping, teaching, and scientific exploration.