Math Reasoning MCP

A Model Context Protocol (MCP) server that gives AI agents a deterministic mathematical brain. Instead of hallucinating algebra, the agent delegates symbolic computation to a SymPy engine over JSON-RPC — getting exact, verifiable results every time.

Built for engineers, researchers, and students working in Algebra, Calculus, Signals & Systems, and Digital Signal Processing (DSP).

Why This Exists

Large Language Models are statistical text predictors. They can describe a Laplace transform, but when asked to execute one — especially a multi-step derivation involving chain rules, frequency shifts, and polynomial expansion — they routinely:

• Drop negative signs

• Hallucinate polynomial terms

• Produce plausible-looking but wrong final answers

Models with built-in code execution (like Gemini) work around this by writing and running Python scripts in a proprietary sandbox. This MCP provides the same deterministic SymPy power as a universal, open standard — pluggable into any AI agent, no proprietary sandbox required.

Related MCP server: ReasonForge

Proof of Value

Problem: Compute the Laplace Transform of $f(t) = t^2 e^{-3t} \sin(2t)$ and find the system poles.

21 Tools

📐 Core Algebra & Calculus

📡 Signals & Continuous Transforms

💻 Discrete-Time & DSP

⚙️ System Analysis

Both check_linearity and check_time_invariance support:

• Memoryless systems: expressions in x and t (e.g., x**2, t*x + 5)

• Difference equations: expressions in x_n, x_n1, x_n2, n (e.g., x_n + x_n1)

check_linearity also detects affine systems (constant offset makes it non-linear).

🧮 Step-by-Step Solvers

These tools don't just return the answer — they show the derivation, making them ideal for education and debugging.

Testing

55 tests covering all 21 tools, edge cases, and error handling:

uv run pytest tests/ -v

tests/test_tools.py   55 passed in 4.38s

Tests cover:

• All core algebra operations (simplify, solve, differentiate, integrate)

• All transform tools (Laplace, Fourier, Z-transform, FFT)

• System analysis (linearity, time-invariance) for both memoryless and memory systems

• Affine vs. linear detection

• Step-by-step output structure

• Structured error handling for malformed input

Installation

Requires uv.

git clone https://github.com/TeleEng/math-reasoning-mcp.git
cd math-reasoning-mcp
uv sync

Usage

Add to your MCP client configuration:

{
  "mcpServers": {
    "math-reasoning": {
      "command": "uv",
      "args": [
        "run",
        "--directory",
        "<absolute-path-to-this-repository>",
        "math-reasoning-mcp"
      ]
    }
  }
}

Compatible with any MCP client: Claude Desktop, Cursor, Windsurf, Antigravity, custom agents, etc.

Architecture

┌──────────────┐     JSON-RPC      ┌──────────────────┐
│   AI Agent   │ ◄──── MCP ──────► │  Math MCP Server │
│  (any LLM)   │   (stdio/SSE)     │   (SymPy+NumPy)  │
└──────────────┘                   └──────────────────┘
       │                                    │
  Reads files,                        Deterministic
  understands                         symbolic math
  context                             engine

Tech Stack

• SymPy — Symbolic mathematics engine

• NumPy — Numerical computation (FFT, quantization)

• MCP SDK — Model Context Protocol server framework

• uv — Fast, reproducible Python dependency management

• pytest — Test framework (55 tests)

License

MIT