Pynite MCP Server

Pynite MCP Server

Enables AI assistants to perform structural engineering analysis using the Pynite finite element library, including model creation, element definition, analysis execution, and result visualization through natural language.

Category
访问服务器

README

Pynite MCP Server

Model Context Protocol (MCP) server for Pynite finite element analysis library. This server exposes Pynite's structural engineering capabilities as tools and resources that can be used by AI assistants like Claude.

Features

  • Model Management: Create, manage, and switch between multiple FE models
  • Element Creation: Add nodes, materials, sections, and structural elements
  • Analysis Tools: Execute structural analysis and extract results
  • Visualization: Generate plots and diagrams with base64 image responses
  • State Management: Proper isolation between requests with metadata tracking

Installation

From Source

git clone https://github.com/buildwellai/MCP-Pynite.git
cd MCP-Pynite
pip install -e .

Prerequisites

  • Python 3.10 or higher
  • PyniteFEA library
  • PyVista for visualization (with VTK backend)

Quick Start

1. Start the MCP Server

# Using SSE transport (HTTP)
python -m pynite_mcp.main

# Using stdio transport (pipe)
TRANSPORT=stdio python -m pynite_mcp.main

2. Connect from Claude Desktop

Add to your MCP configuration:

{
  "mcpServers": {
    "pynite": {
      "command": "python",
      "args": ["-m", "pynite_mcp.main"],
      "transport": "sse",
      "url": "http://localhost:8053"
    }
  }
}

3. Basic Usage Example

Through Claude or another MCP client:

1. Create a new model: create_model("simple_beam")
2. Add material: add_material_tool("steel", E=200e9, G=80e9, nu=0.3, rho=7850)
3. Add nodes: add_node_tool("N1", 0, 0, 0)
4. Add sections and members...
5. Analyze and visualize results

Available Tools

Model Management

  • create_model(name, force=False) - Create new FE model
  • list_models() - List all models with metadata
  • set_current_model(name) - Switch active model
  • get_model_info(model_name=None) - Get detailed model info
  • delete_model(name) - Remove model and free resources

Element Creation

  • add_node_tool(name, X, Y, Z) - Add structural node
  • add_material_tool(name, E, G, nu, rho, fy=None) - Add material
  • add_section_tool(name, material, type, ...) - Add cross-section

Information Retrieval

  • list_nodes_tool(model_name=None) - List all nodes
  • list_materials_tool(model_name=None) - List all materials

Configuration

Environment variables:

HOST=0.0.0.0          # Server host
PORT=8053             # Server port  
TRANSPORT=sse         # Transport type (sse or stdio)
DEBUG=true            # Enable debug logging

Architecture

  • Context Management: Enhanced PyniteContext handles model isolation and metadata
  • Tool Registration: FastMCP decorators expose async functions as MCP tools
  • Error Handling: Comprehensive validation and error reporting
  • Resource Cleanup: Automatic cleanup on session end

Development

Running Tests

pip install -e .[dev]
pytest tests/

Code Quality

black pynite_mcp/
flake8 pynite_mcp/
mypy pynite_mcp/

Examples

See the examples/ directory for complete structural analysis workflows using the MCP server.

Contributing

  1. Fork the repository
  2. Create a feature branch
  3. Add tests for new functionality
  4. Submit a pull request

License

MIT License - see LICENSE file for details.

Links

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选