pyNastran MCP Server
An MCP server that enables AI agents to interact with Nastran FEA models by reading, writing, and analyzing BDF and OP2 files. It provides tools for mesh quality assessment, geometric analysis, and automated report generation for structural engineering workflows.
README
pyNastran MCP Server
A Model Context Protocol (MCP) Server for pyNastran, built with FastMCP. Enables AI agents to interact with Nastran FEA models.
Features
- 🔧 BDF Tools: Read, write, and analyze Nastran input files
- 📊 OP2 Tools: Extract results from Nastran output files
- 🔍 Geometry Tools: Mesh quality checks and geometric analysis
- 📝 Analysis Tools: Automated report generation
- 🚀 FastMCP: Built with modern FastMCP framework
- 🌐 Multiple Transports: stdio, SSE, and streamable-http
Installation
pip install pynastran-mcp
Or install from source:
git clone https://github.com/Shaoqigit/pynastran-mcp.git
cd pynastran-mcp
pip install -e .
Quick Start
Stdio Transport (Default)
For MCP clients like Cherry Studio, Claude Desktop:
pynastran-mcp
SSE Transport
# Default: host=127.0.0.1, port=8080
pynastran-mcp --transport sse
# Custom host and port
pynastran-mcp --transport sse --host 0.0.0.0 --port 8080
Streamable HTTP Transport (Production)
# Default: host=127.0.0.1, port=8080
pynastran-mcp --transport streamable-http
# Custom host and port
pynastran-mcp --transport streamable-http --host 0.0.0.0 --port 8080
MCP Client Configuration
Cherry Studio / Cursor / Claude Desktop
Add to your MCP client configuration:
{
"mcpServers": {
"pynastran": {
"command": "pynastran-mcp"
}
}
}
See CHERRY_STUDIO_TUTORIAL.md for detailed setup instructions.
Available Tools
BDF Tools
| Tool | Description |
|---|---|
read_bdf |
Read BDF file and return model summary |
get_model_info |
Get detailed model information |
write_bdf |
Write model to new BDF file |
get_nodes |
Get node coordinates |
get_elements |
Get element connectivity |
get_materials |
Get material properties |
get_properties |
Get property definitions |
OP2 Tools
| Tool | Description |
|---|---|
read_op2 |
Read OP2 result file |
get_result_cases |
List available result cases |
get_stress |
Extract stress results |
get_displacement |
Extract displacement results |
Geometry Tools
| Tool | Description |
|---|---|
check_mesh_quality |
Check mesh quality metrics |
get_model_bounds |
Get model bounding box |
Analysis Tools
| Tool | Description |
|---|---|
generate_report |
Generate comprehensive analysis report |
Usage Examples
With AI Agents
Once configured, you can ask your AI assistant:
"Read the BDF file at /path/to/model.bdf and tell me about the mesh"
"Analyze the stress results from /path/to/results.op2"
"Check the mesh quality and suggest improvements"
"Generate a report for my Nastran model"
Programmatic Usage
from pynastran_mcp.tools.bdf_tools import BdfTools
from pynastran_mcp.tools.op2_tools import Op2Tools
async def analyze_model():
# BDF Analysis
bdf_tools = BdfTools()
summary = await bdf_tools.read_bdf("wing.bdf")
print(summary)
# OP2 Results
op2_tools = Op2Tools()
stresses = await op2_tools.get_stress("results.op2", element_type="CQUAD4")
print(stresses)
Project Structure
pynastran-mcp/
├── pynastran_mcp/
│ ├── __init__.py
│ ├── server.py # FastMCP server with all tools
│ └── tools/
│ ├── __init__.py
│ ├── bdf_tools.py # BDF file operations
│ ├── op2_tools.py # OP2 result operations
│ ├── geometry_tools.py # Mesh quality checks
│ └── analysis_tools.py # Report generation
├── pyproject.toml
├── README.md
└── examples/
└── example_usage.py
Requirements
- Python 3.10+
- pyNastran >= 1.4.0
- mcp >= 1.0.0 (with FastMCP)
Development
# Setup
git clone https://github.com/Shaoqigit/pynastran-mcp.git
cd pynastran-mcp
pip install -e ".[dev]"
# Run tests
pytest
# Code formatting
black pynastran_mcp/
License
MIT License - see LICENSE file
Acknowledgments
- pyNastran - The underlying Nastran interface library
- MCP Python SDK - FastMCP framework
Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
- Fork the repository
- Create your feature branch (
git checkout -b feature/amazing-feature) - Commit your changes (
git commit -m 'Add some amazing feature') - Push to the branch (
git push origin feature/amazing-feature) - Open a Pull Request
Please make sure to update tests as appropriate and follow the existing code style.
- pyNastran - The underlying Nastran interface library
- MCP Python SDK - FastMCP framework
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