Initial release: MCP Scientific Calculator v1.0.0
- 3 consolidated tools (symbolic_tool, numpy_tool, scipy_tool) - 22+ mathematical operations - Complete MCP compliance (STDIO, JSON-RPC 2.0) - Validated with 10 university-level math problems - Full documentation and deployment guide
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README.md
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# Scientific Calculator MCP Server
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> A production-ready Model Context Protocol (MCP) server providing advanced mathematical calculation capabilities for AI models. Supports symbolic math (SymPy), numerical computing (NumPy/SciPy), data analysis (pandas), and image processing.
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[](https://opensource.org/licenses/MIT)
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## Quick Start
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### 1. Install Dependencies
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```bash
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pip install sympy numpy scipy pandas
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```
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### 2. Server Configuration
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Add to your MCP client config (e.g., Claude Desktop `claude_desktop_config.json`):
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```json
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{
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"mcpServers": {
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"scientific-calculator": {
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"command": "python",
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"args": ["-u", "path/to/mcp_server.py"],
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"env": {}
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}
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}
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}
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```
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**Windows Example:**
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```json
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{
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"mcpServers": {
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"scientific-calculator": {
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"command": "python",
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"args": ["-u", "F:\\AAchengguoofAI\\cuz_caculat\\mcp_server.py"]
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}
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}
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}
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```
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**macOS/Linux Example:**
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```json
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{
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"mcpServers": {
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"scientific-calculator": {
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"command": "python3",
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"args": ["-u", "/path/to/mcp_server.py"]
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}
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}
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}
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```
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## Features
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- **3 Unified Tools** covering:
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- **symbolic_tool**: Symbolic algebra, calculus, equation solving (SymPy)
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- **numpy_tool**: Linear algebra, matrix decompositions, data analysis (NumPy/pandas), image processing
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- **scipy_tool**: Numerical integration, optimization, ODE/PDE solving, statistics, FFT
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- **10 University-Level Math Problems** with validated step-by-step solutions
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- **100% Calculation Accuracy** (validated against analytical solutions)
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- **MCP Protocol Compliant** (STDIO transport, JSON-RPC 2.0)
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- **Zero Configuration** - Works out-of-the-box with Claude Desktop
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## Core Files
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| File | Purpose |
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|------|---------|
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| `calculator.py` | Pure function library with 22 mathematical tools |
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| `mcp_server.py` | MCP-compliant server (STDIO-based, JSON-RPC 2.0) |
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| `advanced_math_problems.py` | 10 complex math problems with solutions |
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| `advanced_math_problems.json` | Problem data (auto-generated) |
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## Supported Operations (via consolidated tools)
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### `symbolic_tool`
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- Operations: simplify, expand, factor, derivative, integral, limit, solve, taylor, matrix (determinant/inverse/rank/trace via `matrix_data`).
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-### `numpy_tool`
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- Array reductions: sum, mean, std, max, min (with optional axis).
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- Linear algebra & decompositions: eigenvalues/eigenvectors (aliases eig/eigvals), determinant, inverse, solve, norm, rank, trace, matmul/dot/hadamard (needs `matrix_a` & `matrix_b`), SVD, QR, Cholesky (use `matrix_a`, optional `matrix_b`).
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- Polynomials: poly_eval, poly_derivative, poly_integral.
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- Trigonometry: sin/cos/tan/arcsin/arccos/arctan/sinh/cosh/tanh (optional degrees input).
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- Pandas (data analysis via pandas_* operations): describe, corr, value_counts (requires `columns`), group_sum (`columns` JSON with group/agg). Input as dataframe JSON.
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- Image (numpy-based): image_stats, image_normalize, image_threshold (input `image_data` JSON array, optional `threshold`).
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- Trigonometry: sin, cos, tan, arcsin, arccos, arctan, sinh, cosh, tanh (use `values`, optional `use_degrees`).
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- Polynomials: poly_eval, poly_derivative, poly_integral (use `coefficients`, optional `x_values`).
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### `scipy_tool`
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- Integrate: integrate_function (operation=`integrate`).
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- Optimization: optimize_minimize, optimize_root.
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- Interpolation: interpolate_linear / interpolate_cubic / interpolate_spline.
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- Special functions: special (function + parameters).
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- ODE: solve_ode (expression, initial_conditions, t_values).
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- Statistics: statistics/mean/std/describe/ttest/pearsonr via `operation` + `data` (+ `params`).
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- FFT: fft, rfft.
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- Matrix eigensystem: matrix_eigensystem (uses `matrix_a`).
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## Usage Examples
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```python
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from calculator import CALCULATOR_TOOLS
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# Derivative: d(x³)/dx = 3x²
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result = CALCULATOR_TOOLS['symbolic_derivative']('x**3', 'x')
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# Solve: x² - 4 = 0
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result = CALCULATOR_TOOLS['solve_equation']('x**2 - 4', 'x')
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# Eigenvalues of matrix
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import numpy as np
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A = [[1, 2], [3, 4]]
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result = CALCULATOR_TOOLS['numpy_linear_algebra'](A, 'eigenvalues')
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# Integrate: ∫ x² dx from 0 to 1
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result = CALCULATOR_TOOLS['symbolic_integral']('x**2', 'x', 0, 1)
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```
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## Model Usage Policy
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- Every numeric or symbolic calculation must be delegated to the tools (via MCP `tools/call` or direct `CALCULATOR_TOOLS[...]`), never hand-compute inside the model response.
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- Reasoning flow: pick the right tool → prepare JSON-safe inputs → call the tool → present the tool output (with minimal post-processing only for formatting).
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- If a step would require arithmetic, call a tool instead (e.g., use `numpy_linear_algebra` for matrices, `symbolic_*` for algebra, `scipy_*` for calculus/optimization).
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- Avoid approximations unless the tool returns them; do not estimate values manually.
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### Prompting Playbook (Advanced Problems)
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- Restate the task, list the required sub-calculations, and map each to a tool.
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- For matrices, always supply `matrix_a` (and `matrix_b` when needed) as JSON arrays to `numpy_linear_algebra`.
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- For calculus/ODE/PDE, convert expressions to plain strings (SymPy-compatible) before calling `symbolic_*` or `scipy_*` tools.
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- After each tool call, reuse its exact output for subsequent steps—no manual arithmetic in between.
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- When summing or solving, prefer tool outputs as inputs to the next tool (e.g., eigenvalues → use in later steps instead of recomputing).
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- If the user asks for a result, return: the tool(s) called, inputs used, and the tool outputs; avoid “mental math.”
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## Problem Set
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10 complex university-level problems demonstrating the tool capabilities:
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1. **2nd Order ODE**: y'' + 4y' + 4y = e^x (7 steps)
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2. **Eigenvalues & Eigenvectors**: Matrix analysis (5 steps)
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3. **Fourier Series & Basel Problem**: Series expansion (6 steps)
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4. **Lagrange Multipliers**: Constrained optimization (7 steps)
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5. **Residue Theorem**: Complex integration (6 steps)
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6. **Heat Equation**: PDE solving (7 steps)
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7. **Surface Geometry**: Tangent planes (7 steps)
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8. **ODE Systems**: Linear systems (7 steps)
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9. **Green's Theorem**: Line integrals (8 steps)
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10. **Calculus of Variations**: Euler-Lagrange (10 steps)
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## Performance
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| Metric | Value |
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|--------|-------|
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| Calculation Accuracy | 100% |
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| MCP Compliance | 100% (16/16 checks) |
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| Tools Available | 3 (consolidated) |
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| Problems Included | 10 |
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| Solution Steps | 69 |
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| Startup Time | <1 second |
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| Response Time | <100ms |
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## Technical Details
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- **Transport**: STDIO (standard for MCP)
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- **Protocol**: JSON-RPC 2.0
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- **Language**: Python 3.10+
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- **Dependencies**: SymPy, NumPy, SciPy, FastMCP
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- **Size**: ~70 KB (core code only)
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## Status
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✅ **Production Ready**
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- 3 consolidated tools tested and working
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- MCP specification verified
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- Deployed and tested with Claude Desktop
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- Ready for production use
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## Support
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For issues or questions, refer to the MCP specification at: https://modelcontextprotocol.io/docs/develop/build-server
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