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abhiphile/fermat-mcp

by Various

๐Ÿš€ Fermat MCP: The Ultimate Math Engine - Unifying SymPy, NumPy & Matplotlib in one powerful server! Perfect for devs & researchers.

A

MCP

abhiphile/fermat-mcp

Added 1 June 2026

#mathematics #matplotlib #mcp #mcp-server #numerical-computation #numpy #symbolic-computation #sympy

Overview

Fermat MCP is a Python-based Model Context Protocol server that integrates SymPy, NumPy, and Matplotlib into a single math engine. It allows AI agents to perform symbolic and numerical computations and generate plots through a unified interface.

Best for

Best for
Developers and researchers who want to give AI agents direct access to symbolic math, numerical computing, and plotting.

Use cases

  • Perform symbolic algebra and calculus via SymPy through an AI agent
  • Run numerical computations and array operations with NumPy
  • Generate and return Matplotlib plots from mathematical expressions

Notes

Fermat MCP is a Python-based Model Context Protocol server that integrates SymPy, NumPy, and Matplotlib into a single math engine. It allows AI agents to perform symbolic and numerical computations and generate plots through a unified interface.

16 stars on GitHub. Last updated 2025-10-08. Licensed MIT.

Use cases

  • Perform symbolic algebra and calculus via SymPy through an AI agent
  • Run numerical computations and array operations with NumPy
  • Generate and return Matplotlib plots from mathematical expressions

Pros

  • Combines three major Python math libraries in one server
  • Enables AI agents to do math and plotting without separate tools
  • Lightweight and easy to set up for developers

Cons

  • Small community with only 16 GitHub stars
  • Limited documentation and examples beyond the repository
  • Requires Python environment and dependency management

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Combines three major Python math libraries in one server
  • Enables AI agents to do math and plotting without separate tools
  • Lightweight and easy to set up for developers

Cons

  • Small community with only 16 GitHub stars
  • Limited documentation and examples beyond the repository
  • Requires Python environment and dependency management