FEA-MCP Server: Unifying Finite Element Analysis with Model Context Protocol

Overview: What is FEA-MCP Server?

The FEA-MCP Server provides a unified API interface for interacting with various Finite Element Analysis (FEA) software packages, including mainstream tools like ETABS and LUSAS. This innovative solution facilitates AI control over complex FEA modeling, analysis, and post-processing tasks through a consistent and standardized Model Context Protocol (MCP). By enabling seamless integration between AI applications and FEA tools, it empowers developers to build smart, flexible workflows that enhance productivity and accuracy in engineering design.

🔧 Core Features & MCP Capabilities

The FEA-MCP Server offers a wide array of capabilities through its API interface. It supports multiple FEA software packages, with current support for ETABS and LUSAS. The server facilitates geometric modeling, including operations on points/joints, lines/frames/beams-columns, volumes/solids, and more advanced functions like sweeping objects to create new geometries. Additionally, the server allows reading model units and selecting specific objects based on their type.

The core features of the FEA-MCP Server are seamlessly integrated with MCP capabilities, ensuring that AI applications can reliably interact with different FEA tools without needing to familiarize themselves with each software's unique interface. This interoperability opens up new possibilities for automated design, analysis, and optimization in complex engineering projects.

Real-World Use Cases

Use Case 1: Automated Structural Analysis Imagine an architecture firm using both ETABS and LUSAS for various structural analyses. With the FEA-MCP Server, AI models can dynamically switch between these tools based on specific tasks and project requirements. For instance, during early design stages, an AI model could define a complex structure by creating points and volumes in ETABS. Later, it might transfer this model to LUSAS for detailed analysis using advanced features like surface and volume sweeping.

Use Case 2: Adaptive Load Testing In the context of continuous engineering projects where dynamic load scenarios need frequent updates, AI can leverage the FEA-MCP Server's capabilities to redefine load patterns interactively. For example, an ongoing bridge construction project might involve periodic modifications to support structures due to environmental factors or design changes. Using MCP, the AI system could seamlessly redefine loads on specific elements (like frames and solid sections) within LUSAS without disrupting ongoing analysis.

⚙️ MCP Architecture & Protocol Implementation

The FEA-MCP Server is built upon the Model Context Protocol (MCP), a standardized communication layer that allows various software applications to interact with each other in a unified manner. The architecture of this server includes several key components:

MCP Protocol Flow Diagram

graph TD;
    A[AI Application] -->|MCP Client| B[MCP Protocol];
    B --> C[MCP Server];
    C --> D[Data Source/Tool];
    style A fill:#e1f5fe;
    style C fill:#f3e5f5;
    style D fill:#e8f5e8;

MCP Client Compatibility Matrix

MCP Client	Resources	Tools	Prompts	Status
Claude Desktop	✅	✅	✅	Full Support
Continue	✅	✅	✅	Full Support
Cursor	❌	✅	❌	Tools Only

This matrix highlights the robust compatibility of the FEA-MCP Server with major MCP clients, ensuring seamless integration and efficient workflow.

🚀 Getting Started with Installation

Requirements & Guide

The installation process for the FEA-MCP Server requires a Windows operating system and certain Python libraries. Below are detailed steps to get started:

1. Install Required Libraries:

   pip install pywin32>=228 comtypes>=1.4.0 mcp>=0.1.0
   # or
   pip install -r requirements.txt
   

2. Download and Extract Files: Save the repository files locally, for example at C:\your_path_to_the_extracted_server\FEA-MCP\.

3. Configure MCP Server: Modify the config.json file located in the src directory to define server details like name, version, and FEA software.

4. Install AI Client: Ensure a compatible AI client is installed, such as Claude Desktop or other MCP-supported applications.

5. Launch MCP Server Automatically: Configure your chosen AI client to start the FEA-MCP Server at launch.

6. Verify Installation: Test the server's functionality using the provided commands.

Sample Configuration

Here’s an example of how to configure the config.json file:

{
    "server": {
        "name": "FEA MCP",
        "version": "1.0.0"
    },
    "fea": {
        "software": "LUSAS",
        "version": "21.1"
    }
}

This configuration sets the server to use LUSAS v21.1 by default.

💡 Key Use Cases in AI Workflows

The FEA-MCP Server significantly enhances AI-driven workflows by providing developers with a versatile platform for integrating machine learning and deep learning into engineering tasks. By leveraging MCP, AI models can dynamically interact with complex FEA tools, enabling real-time model optimization, adaptive design, and comprehensive analysis.

Through the unified interface provided by the FEA-MCP Server, engineers can achieve several key benefits:

• Flexibility: Switch between different FEA software seamlessly based on project requirements.
• Efficiency: Automate repetitive tasks, such as object creation and geometric transformations.
• Accuracy: Obtain precise results from complex models managed directly through AI.

🔌 Integration with MCP Clients

The FEA-MCP Server supports integration with popular MCP clients like Claude Desktop, Continue, and Cursor. Here’s how to set up these clients:

Claude Desktop

To configure Claude Desktop for the FEA-MCP Server:

1. Open File > Settings > Developer > Edit Config.
2. Modify claude_desktop_config.json to include server details.
3. Restart Claude Desktop.

Continue and Cursor

For continued support from other MCP clients, follow similar steps by updating configuration files to point to the FEA-MCP Server's launch command.

📊 Performance & Compatibility Matrix

The compatibility matrix of the FEA-MCP Server ensures that developers can use it with a variety of AI clients and tools without performance issues. The server supports full integration with Claude Desktop, Continue, and Cursor, making it suitable for a wide range of applications in engineering and design.

🛠️ Advanced Configuration & Security

Advanced users can customize the FEA-MCP Server according to specific needs using the config.json file. For security and optimization purposes, ensure that API keys are properly managed and protected. Additionally, consider setting up logging for debugging and monitoring server activity.

Sample MCP Configuration Code

{
    "mcpServers": {
        "FEA-MCP": {
            "command": "python",
            "args": [
                "C:\\your_path_to_the_extracted_server\\FEA-MCP\\src\\server.py"
            ],
            "env": {
                "API_KEY": "your-api-key"
            }
        }
    }
}

❓ Frequently Asked Questions (FAQ)

1. How does the FEA-MCP Server ensure seamless integration with multiple FEA software packages?

The server leverages the Model Context Protocol (MCP) to provide a standardized interface, allowing it to interact seamlessly with both ETABS and LUSAS.

2. Can I use the FEA-MCP Server with other AI clients besides Claude Desktop?

Yes, other MCP-compatible clients like Continue and Cursor can also utilize the FEA-MCP Server for enhanced functionality.

3. Are there any performance implications while using the FEA-MCP Server with multiple AI applications simultaneously?

Minimal performance impact is observed; however, managing concurrent connections efficiently ensures smooth operation during complex workflows.

4. How does the server handle security and API key management?

API keys should be stored securely to prevent unauthorized access. Regular audits of access logs help in maintaining secure operations.

5. Can developers contribute to improving the FEA-MCP Server's functionality and features?

Absolutely, contributions are welcome. Developers can fork the repository, make improvements, and submit pull requests for review.

👨‍💻 Development & Contribution Guidelines

Open-source contributors can clone the project from GitHub and explore the codebase to identify areas for improvement. Detailed instructions on setting up a development environment and contributing back to the community are provided in the CONTRIBUTING.md file.

🌐 MCP Ecosystem & Resources

For more information about the Model Context Protocol (MCP) ecosystem, visit the official MCP documentation site or explore other resources related to MCP integrations. Engaging with this vibrant community can provide valuable insights and support for your projects.

By integrating the FEA-MCP Server into your AI workflows, you can unlock new possibilities in engineering design and analysis, enhancing both efficiency and accuracy in your projects.