mathematica-mcp-full 0.9.1

Full GUI control of Mathematica notebooks and kernel via Model Context Protocol

Mathematica MCP

Turn Mathematica into a first-class tool for AI agents.

A local MCP server that lets AI agents run Mathematica, control notebooks, and verify results. Works with Claude, Cursor, VS Code, Codex, and Gemini.

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Watch it in action

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 Mathematica code, but they can't run it, verify it, or interact with live notebooks. This MCP server bridges that gap:

• Live notebook control: create, edit, evaluate, and screenshot Mathematica notebooks directly from your AI agent
• Symbolic + numeric + visual in one MCP: ~82 tools covering algebra, calculus, plotting, data import/export, Wolfram Alpha, and interactive UIs
• Agent-optimized: compact response shaping, session state tools, and computation journaling designed for how LLM agents actually work
• Error-aware execution: Mathematica errors and warnings are returned directly to the agent, so it can debug without you manually copying notebook output back into chat
• Local and private: core execution runs on your machine — optional tools like wolfram_alpha and repository search contact Wolfram's cloud services when invoked

Ask your agent for a derivation, a 3D plot, a notebook edit, or a verification step, and it can actually do it.

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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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What You Can Ask For

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

execute_code("Integrate[x^2 Sin[x], {x, 0, Pi}]")  =>  -4 + Pi^2
verify_derivation(steps=["Integrate[...", "-4 + Pi^2"])  =>  All steps valid

"Plot the sombrero function in a new notebook."

create_notebook(title="Sombrero")
execute_code("Plot3D[Sinc[Sqrt[x^2+y^2]], {x,-4,4}, {y,-4,4}]", style="notebook")
=> [3D surface plot rendered in live notebook]

"Interactive: slider for Sin[n x]"

execute_code("Manipulate[Plot[Sin[n x],{x,0,2Pi}],{n,1,10}]", style="interactive")
=> [Live slider UI in Mathematica frontend]

Beyond these: data import/export (hundreds of formats), Wolfram Alpha queries, notebook reading/analysis, symbolic debugging, and more. See the Technical Reference for the full tool list.

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How It Compares

Capability	Plain LLM	Copy-paste to Mathematica	This MCP
Write Mathematica code	Yes	Yes	Yes
Verify math results	May hallucinate	Manual	One tool call
Iterate on errors	Guesses without running	Manual loop	Errors fed back with tips
Structured results	No	No	Yes, with metadata
Generate and view plots	No	Manual	File or notebook
Live notebook control	No	No	Create/edit/eval/screenshot
Interactive UIs (sliders)	No	Manual	Yes, in Mathematica
Read notebooks offline	No	No	Yes, Python parser
Private / local execution	N/A	Yes	Yes*

*Core computation runs locally. Optional tools (wolfram_alpha, repository search) contact Wolfram cloud services when invoked.

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Quick Start

From install to first working notebook plot in under 2 minutes.

Prerequisites

1. Mathematica 14.0+ with wolframscript in your PATH

   • Download Mathematica
   • macOS: Add to ~/.zshrc: export PATH="/Applications/Mathematica.app/Contents/MacOS:$PATH"

2. uv package manager

   curl -LsSf https://astral.sh/uv/install.sh | sh
   

One-Command Setup

# For Claude Desktop
uvx mathematica-mcp-full setup claude-desktop

# For Cursor
uvx mathematica-mcp-full setup cursor

# For VS Code (requires GitHub Copilot Chat extension)
uvx mathematica-mcp-full setup vscode

# For OpenAI Codex CLI
uvx mathematica-mcp-full setup codex

# For Google Gemini CLI
uvx mathematica-mcp-full setup gemini

# For Claude Code CLI
uvx mathematica-mcp-full setup claude-code

# Optional: select a tool profile (default is "full")
uvx mathematica-mcp-full setup claude-desktop --profile notebook

Then restart Mathematica and your editor. Done!

VS Code: Alternative setup via Command Palette

Prerequisite: GitHub Copilot Chat extension must be installed - MCP support is built into Copilot.

1. Press Cmd+Shift+P (Mac) / Ctrl+Shift+P (Windows)
2. Type "MCP" -> Select "MCP: Add Server"
3. Choose "Command (stdio)": not "pip"
4. Enter command: uvx
5. Enter args: mathematica-mcp-full
6. Name it: mathematica
7. Choose scope: Workspace or User

Alternative: Interactive Installer

bash <(curl -sSL https://raw.githubusercontent.com/AbhiRawat4841/mathematica-mcp/main/install.sh)

Verify Installation

uvx mathematica-mcp-full doctor

Tip: If you encounter errors after updating, clear the cache:

uv cache clean mathematica-mcp-full && uvx mathematica-mcp-full setup <client>

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Execution Styles

Control where results appear with natural language or the style parameter:

Say this...	style=	What happens
"calculate", "compute", "evaluate", "solve", "what is"	"compute"	Result appears as text in chat
"plot", "show", "graph", "visualize", "in notebook"	"notebook"	Executes in the current Mathematica notebook
"new notebook", "fresh notebook", "create notebook"	two-step	create_notebook() then execute_code(style="notebook")
"interactive", "manipulate", "slider", "dynamic", "animate"	"interactive"	Live front-end evaluation (sliders, animations)

If you don't include a keyword, the default depends on your tool profile.

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Tool Profiles

Choose how many tools to expose:

Profile	Tools	Best for
math	~28	Pure computation, no notebook UI
notebook	~48	+ notebook read/write/screenshot
full (default)	~82	+ advanced notebook ops, repositories, admin

Pass --profile during setup or set MATHEMATICA_PROFILE env var.

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Built for Agent Workflows

The server is designed for how LLM agents actually work: long conversations with context limits, intermittent failures, and token budgets:

Feature	What it does	How to use
Compact Responses	Strip verbose metadata, keep essentials. Saves tokens.	response_detail="compact" on execute_code
Session Brief	One-call snapshot: connection status, recent errors, routing advice	get_session_brief()
Computation Journal	Ring buffer of recent computations that helps agents recover context across long conversations	get_computation_journal()
Smart Caching	Pure expressions (Sin[Pi]) survive variable mutations without re-evaluation	Always on
Diagnostic Mode	Full response + cache epoch + routing hints for debugging	response_detail="diagnostic"

Routing Intelligence (opt-in)

For power users, the server can learn from transport outcomes and adapt:

# Observe mode: collect stats, no behavior change
export MATHEMATICA_ROUTING_MEMORY=observe

# Advise mode: + routing hints + enables adaptive routing
export MATHEMATICA_ROUTING_MEMORY=advise
export MATHEMATICA_ROUTING_ACTION=compute_cli_skip  # optional: skip failing transport

The adaptive routing circuit-breaker automatically skips persistently failing compute CLI transport with half-open probe recovery. See the Technical Reference for details.

Privacy: Routing memory stores only aggregate counters; the in-memory journal stores short code/output previews (not persisted). Notebook extraction results are cached to ~/.cache/mathematica-mcp/notebooks/ with mtime-based invalidation; delete the directory to clear the cache.

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Manual Installation

For full details, troubleshooting, and advanced configuration, see the Installation Guide.

Click to expand quick manual setup

1. Clone & Install:

   git clone https://github.com/AbhiRawat4841/mathematica-mcp.git
   cd mathematica-mcp
   uv sync
   

2. Install Mathematica Addon:

   wolframscript -file addon/install.wl
   

   Restart Mathematica after this step.

3. Configure your editor: add the MCP server to your client's config file. See the Installation Guide for Claude Desktop, Cursor, VS Code, and other client configs.

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Documentation

• Technical Reference: Architecture, tools, and configuration
• Security Model: Threat model, permissions, and vulnerability reporting
• Benchmarks: Performance data and reproduction steps
• Contributing: Development setup, testing, and PR process
• Changelog: Version history
• Examples: Polished agent session walkthroughs

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License

MIT License