Mathematica

A front-end / notebook automation layer for Mathematica, built for AI agents.

Your AI agent can write Mathematica code. This server lets it run that code in your live Mathematica session: create and edit notebooks, build interactive Manipulate panels, capture screenshots, verify derivations step by step, and read .nb files even without a kernel. Works with Claude, Cursor, VS Code, Codex, and Gemini.

It runs beside the official Wolfram Local MCP, not instead of it: Wolfram's server is the reference evaluator and documentation surface; this one owns the live notebook. See How it compares.

License: MIT Python 3.10+ Mathematica 14+ CI Repo Published

Upgrading from an earlier version? See the Migration Guide.

Watch it in action

Mathematica MCP Demo

An AI agent solving math, generating plots, and controlling a live Mathematica notebook. Errors are returned directly to the agent, no copy-pasting notebook output back into chat.

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Why this exists

LLMs can write Wolfram Language, but they cannot run it, see the result, or fix their own mistakes. This server closes that loop:

• Live notebook control: the agent creates, edits, evaluates, and screenshots real notebooks in your running Mathematica front end. Interactive content (Manipulate, Dynamic, sliders) renders as live panels.
• Fast by default: computation runs on a persistent kernel session that starts warming the moment the server launches; calls return in milliseconds, not the ~13 seconds of a cold wolframscript start per request.
• Self-debugging agents: Mathematica errors flow back with a suggested_fix and, when derivable, a ready-to-run retry_with call. No copying red text from the notebook into chat.
• Derivation checking: verify_derivation validates a chain of mathematical steps and pinpoints the first invalid one.
• Reads notebooks without a license: read_notebook_file parses .nb files in pure Python when no kernel is available (a kernel is used for higher fidelity when present).
• Local and private: core execution stays on your machine. Cloud services are contacted only by opt-in tools like wolfram_alpha.

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What you can ask for

"Integrate x^2 sin(x) from 0 to pi, then verify the result."

evaluate("Integrate[x^2 Sin[x], {x, 0, Pi}]")   =>  -4 + Pi^2
verify_derivation(steps=["Integrate[x^2 Sin[x], {x, 0, Pi}]", "-4 + Pi^2"])
=> Step 1 → 2: ✓ VALID
   All steps are valid!

"Plot the sombrero function in a new notebook."

notebooks(action="create", title="Sombrero")
evaluate("Plot3D[Sinc[Sqrt[x^2+y^2]], {x,-4,4}, {y,-4,4}]", target="notebook")
=> [3D surface plot rendered in the live notebook]

"Give me a Chebyshev polynomial explorer with a degree slider."

evaluate("Manipulate[Plot[ChebyshevT[n, x], {x, -1, 1}], {n, 0, 30, 1}]", target="notebook")
=> [live slider panel in the notebook; interactive code is auto-routed to the front end]

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Quick start

Prerequisites: Mathematica 14.0+ (15+ recommended) with wolframscript on your PATH, and the uv package manager.

# One command, pick your client:
uvx mathematica-mcp-full setup claude-desktop   # or: cursor | vscode | codex | gemini | claude-code

Restart Mathematica (so the addon loads) and restart your editor. Then verify:

uvx mathematica-mcp-full doctor

Done - ask your agent for a plot.

The PyPI package and CLI are named mathematica-mcp-full.

Manual installation, per-client configuration details, and troubleshooting live in the Installation Guide.

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The lean default

Agents see a consolidated 12-tool surface (~2.9k tokens of schema) instead of the classic 82 tools (~15k tokens) - a 5x cut in the context an agent pays before doing any work, with the same engine underneath. Prefer everything? MATHEMATICA_PROFILE=classic restores the full pre-1.0 surface, and MATHEMATICA_TOOLSETS adds opt-in extras to lean.

• Tool reference: Technical Reference - Lean Profile Tools
• Profiles and toolsets: Technical Reference - Tool Profiles
• How it stays fast: Technical Reference - Architecture

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How it compares

Runs alongside the official Wolfram Local MCP (setup <client> --with-official configures both side by side). The differentiator is live notebook / front-end automation:

Capability	Official Wolfram Local MCP	This MCP
Wolfram-Language evaluation	WolframLanguageEvaluator	evaluate (warm persistent kernel)
Wolfram Alpha	WolframAlpha	wolfram_alpha (opt-in cloud)
Symbol docs / definitions	SymbolDefinition, CreateSymbolDoc	symbols extra (get_symbol_info)
Read a notebook file	ReadNotebook (needs kernel)	read_notebook_file - works with no kernel / license (Python fallback)
Write a notebook file	WriteNotebook	notebooks, edit_cells (live front-end)
Live notebook control (create/edit/eval/screenshot)	No	Yes
Interactive UIs (sliders, Manipulate)	No	Yes, in the live front-end
Derivation verification	No	verify_derivation
Doc search / code inspection / test reports	CodeInspector, TestReport	Deliberately not duplicated - use the official server

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Who this is for

Audience	Use case
Researchers using LLM coding assistants	Run Mathematica from Claude/Cursor/VS Code without leaving your editor
Data scientists	Import, transform, and visualize data through natural language
Educators	Create interactive Mathematica notebooks through AI conversation
Not for	Production web services, untrusted multi-tenant environments

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Documentation

• Installation Guide: manual setup, per-client configs, troubleshooting
• Migration Guide: upgrading between versions
• Technical Reference: architecture, tools, and configuration
• Security Model: threat model, permissions, and vulnerability reporting
• Benchmarks: performance data and reproduction steps
• Examples: worked agent conversations (symbolic calculus, notebook analysis)
• Contributing: development setup, testing, and PR process
• Changelog: version history

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License

MIT License