ef0b2412e0
- 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
147 lines
4.0 KiB
Markdown
147 lines
4.0 KiB
Markdown
# Quick Start Guide for MCP Scientific Calculator
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## For End Users
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### Installation
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```bash
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pip install sympy numpy scipy pandas
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```
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### Claude Desktop Configuration
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**Windows:**
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1. Open `%APPDATA%\Claude\claude_desktop_config.json`
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2. Add this configuration:
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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:\\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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**macOS/Linux:**
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1. Open `~/Library/Application Support/Claude/claude_desktop_config.json`
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2. Add this configuration:
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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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3. Restart Claude Desktop
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4. You'll see 3 new tools: `symbolic_tool`, `numpy_tool`, `scipy_tool`
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## Available Tools
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### 1. symbolic_tool
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Symbolic mathematics operations:
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- `simplify`, `expand`, `factor` - algebraic manipulation
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- `derivative`, `integral` - calculus
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- `solve` - equation solving
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- `limit`, `taylor` - advanced calculus
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- `matrix` - symbolic matrix operations
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**Example:**
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```
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Calculate the derivative of x³ + 2x²
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→ Use symbolic_tool with operation="derivative", expression="x**3 + 2*x**2", variable="x"
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```
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### 2. numpy_tool
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Numerical computing and data analysis:
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- `eigenvalues`, `eigenvectors`, `determinant`, `inverse` - linear algebra
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- `matmul`, `dot`, `hadamard` - matrix products
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- `svd`, `qr`, `cholesky` - matrix decompositions
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- `sum`, `mean`, `std`, `max`, `min` - array operations
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- `sin`, `cos`, `tan` - trigonometry
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- `pandas_describe`, `pandas_corr`, `pandas_value_counts` - data analysis
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- `image_stats`, `image_normalize`, `image_threshold` - image processing
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**Example:**
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```
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Find eigenvalues of matrix [[1,2],[2,1]]
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→ Use numpy_tool with operation="eigenvalues", matrix_a="[[1,2],[2,1]]"
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```
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### 3. scipy_tool
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Scientific computing:
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- `integrate` - numerical integration
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- `optimize_minimize`, `optimize_root` - optimization
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- `interpolate_linear`, `interpolate_cubic` - interpolation
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- `solve_ode` - differential equations
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- `fft`, `rfft` - signal processing
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- `statistics` - statistical analysis
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**Example:**
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```
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Solve ODE: dy/dt = -2y, y(0)=1, from t=0 to t=5
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→ Use scipy_tool with operation="solve_ode", expression="lambda t, y: -2*y", initial_conditions="[1]", t_values="[0,1,2,3,4,5]"
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```
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## Testing
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Run the verification script:
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```bash
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python verify_production_readiness.py
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```
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Expected output:
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```
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[OK] 工具数量正确
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[OK] 工具名称完整
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[OK] symbolic_tool.derivative 工作正常
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[OK] symbolic_tool.integral 工作正常
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[OK] symbolic_tool.solve 工作正常
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[OK] numpy_tool.determinant 工作正常
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[OK] 问题集完整
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[OK] 所有验证通过 - 生产就绪!
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```
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## Example Problems
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The server includes 10 university-level math problems:
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1. Second-order ODE with initial conditions
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2. Matrix eigenvalue decomposition
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3. Fourier series expansion
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4. Lagrange multipliers (constrained optimization)
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5. Complex integration (residue theorem)
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6. Heat equation (PDE)
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7. Surface tangent planes
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8. Linear ODE systems
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9. Green's theorem (line integrals)
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10. Calculus of variations
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See `advanced_math_problems.py` for complete solutions.
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## Troubleshooting
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**Tools not appearing in Claude Desktop:**
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- Check config file syntax (valid JSON)
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- Verify Python path in "command" field
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- Ensure all dependencies installed: `pip install sympy numpy scipy pandas`
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- Restart Claude Desktop after config changes
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**Import errors:**
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- Install missing packages: `pip install -r requirements.txt`
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- Verify Python version ≥ 3.8
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**Tool execution errors:**
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- Check input format (JSON strings for arrays/matrices)
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- Review operation name spelling
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- Consult README.md for parameter requirements
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## Support
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- GitHub: https://github.com/YOUR_USERNAME/mcp-scientific-calculator
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- Issues: https://github.com/YOUR_USERNAME/mcp-scientific-calculator/issues
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- MCP Docs: https://modelcontextprotocol.io/
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