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
This commit is contained in:
一条懒狗
146
QUICKSTART.md
Normal file
146
QUICKSTART.md
Normal file
@@ -0,0 +1,146 @@
|
||||
# Quick Start Guide for MCP Scientific Calculator
|
||||
|
||||
## For End Users
|
||||
|
||||
### Installation
|
||||
```bash
|
||||
pip install sympy numpy scipy pandas
|
||||
```
|
||||
|
||||
### Claude Desktop Configuration
|
||||
|
||||
**Windows:**
|
||||
1. Open `%APPDATA%\Claude\claude_desktop_config.json`
|
||||
2. Add this configuration:
|
||||
```json
|
||||
{
|
||||
"mcpServers": {
|
||||
"scientific-calculator": {
|
||||
"command": "python",
|
||||
"args": ["-u", "F:\\path\\to\\mcp_server.py"]
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
**macOS/Linux:**
|
||||
1. Open `~/Library/Application Support/Claude/claude_desktop_config.json`
|
||||
2. Add this configuration:
|
||||
```json
|
||||
{
|
||||
"mcpServers": {
|
||||
"scientific-calculator": {
|
||||
"command": "python3",
|
||||
"args": ["-u", "/path/to/mcp_server.py"]
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
3. Restart Claude Desktop
|
||||
4. You'll see 3 new tools: `symbolic_tool`, `numpy_tool`, `scipy_tool`
|
||||
|
||||
## Available Tools
|
||||
|
||||
### 1. symbolic_tool
|
||||
Symbolic mathematics operations:
|
||||
- `simplify`, `expand`, `factor` - algebraic manipulation
|
||||
- `derivative`, `integral` - calculus
|
||||
- `solve` - equation solving
|
||||
- `limit`, `taylor` - advanced calculus
|
||||
- `matrix` - symbolic matrix operations
|
||||
|
||||
**Example:**
|
||||
```
|
||||
Calculate the derivative of x³ + 2x²
|
||||
→ Use symbolic_tool with operation="derivative", expression="x**3 + 2*x**2", variable="x"
|
||||
```
|
||||
|
||||
### 2. numpy_tool
|
||||
Numerical computing and data analysis:
|
||||
- `eigenvalues`, `eigenvectors`, `determinant`, `inverse` - linear algebra
|
||||
- `matmul`, `dot`, `hadamard` - matrix products
|
||||
- `svd`, `qr`, `cholesky` - matrix decompositions
|
||||
- `sum`, `mean`, `std`, `max`, `min` - array operations
|
||||
- `sin`, `cos`, `tan` - trigonometry
|
||||
- `pandas_describe`, `pandas_corr`, `pandas_value_counts` - data analysis
|
||||
- `image_stats`, `image_normalize`, `image_threshold` - image processing
|
||||
|
||||
**Example:**
|
||||
```
|
||||
Find eigenvalues of matrix [[1,2],[2,1]]
|
||||
→ Use numpy_tool with operation="eigenvalues", matrix_a="[[1,2],[2,1]]"
|
||||
```
|
||||
|
||||
### 3. scipy_tool
|
||||
Scientific computing:
|
||||
- `integrate` - numerical integration
|
||||
- `optimize_minimize`, `optimize_root` - optimization
|
||||
- `interpolate_linear`, `interpolate_cubic` - interpolation
|
||||
- `solve_ode` - differential equations
|
||||
- `fft`, `rfft` - signal processing
|
||||
- `statistics` - statistical analysis
|
||||
|
||||
**Example:**
|
||||
```
|
||||
Solve ODE: dy/dt = -2y, y(0)=1, from t=0 to t=5
|
||||
→ Use scipy_tool with operation="solve_ode", expression="lambda t, y: -2*y", initial_conditions="[1]", t_values="[0,1,2,3,4,5]"
|
||||
```
|
||||
|
||||
## Testing
|
||||
|
||||
Run the verification script:
|
||||
```bash
|
||||
python verify_production_readiness.py
|
||||
```
|
||||
|
||||
Expected output:
|
||||
```
|
||||
[OK] 工具数量正确
|
||||
[OK] 工具名称完整
|
||||
[OK] symbolic_tool.derivative 工作正常
|
||||
[OK] symbolic_tool.integral 工作正常
|
||||
[OK] symbolic_tool.solve 工作正常
|
||||
[OK] numpy_tool.determinant 工作正常
|
||||
[OK] 问题集完整
|
||||
[OK] 所有验证通过 - 生产就绪!
|
||||
```
|
||||
|
||||
## Example Problems
|
||||
|
||||
The server includes 10 university-level math problems:
|
||||
1. Second-order ODE with initial conditions
|
||||
2. Matrix eigenvalue decomposition
|
||||
3. Fourier series expansion
|
||||
4. Lagrange multipliers (constrained optimization)
|
||||
5. Complex integration (residue theorem)
|
||||
6. Heat equation (PDE)
|
||||
7. Surface tangent planes
|
||||
8. Linear ODE systems
|
||||
9. Green's theorem (line integrals)
|
||||
10. Calculus of variations
|
||||
|
||||
See `advanced_math_problems.py` for complete solutions.
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
**Tools not appearing in Claude Desktop:**
|
||||
- Check config file syntax (valid JSON)
|
||||
- Verify Python path in "command" field
|
||||
- Ensure all dependencies installed: `pip install sympy numpy scipy pandas`
|
||||
- Restart Claude Desktop after config changes
|
||||
|
||||
**Import errors:**
|
||||
- Install missing packages: `pip install -r requirements.txt`
|
||||
- Verify Python version ≥ 3.8
|
||||
|
||||
**Tool execution errors:**
|
||||
- Check input format (JSON strings for arrays/matrices)
|
||||
- Review operation name spelling
|
||||
- Consult README.md for parameter requirements
|
||||
|
||||
## Support
|
||||
|
||||
- GitHub: https://github.com/YOUR_USERNAME/mcp-scientific-calculator
|
||||
- Issues: https://github.com/YOUR_USERNAME/mcp-scientific-calculator/issues
|
||||
- MCP Docs: https://modelcontextprotocol.io/
|
||||
Reference in New Issue
Block a user