swagger-mcp

Overview

swagger-mcp is a tool designed to scrape Swagger UI by extracting the swagger.json file and dynamically generating well-defined mcp tools at runtime. These tools can be utilized by the MCP client for further tool selection.

📽️ Demo Video

Check out demo video showcasing the project in action:

🙌 Support

If you find this project valuable, please support me on LinkedIn by:

• 👍 Liking and sharing our demo post
• 💬 Leaving your thoughts and feedback in the comments
• 🔗 Connecting with me for future updates

Your support on LinkedIn will help me reach more people and improve the project!

Prerequisites

To use swagger-mcp, ensure you have the following dependencies:

1. LLM Model API Key / Local LLM: Requires access to OpenAI, Claude, or Ollama models.
2. Any MCP Client: (Used mark3labs - mcphost)

Installation and Setup

Follow these steps to install and run swagger-mcp:

go install github.com/danishjsheikh/swagger-mcp@latest
swagger-mcp

MCP Configuration

To integrate with mcphost, include the following configuration in .mcp.json:

{
    "mcpServers":
    {
        "swagger_loader": {
            "command": "swagger-mcp",
            "args": ["<swagger/doc.json_url>"]
        }
    }
}

Demo Flow

1. Some Backend:

   go install github.com/danishjsheikh/go-backend-demo@latest 
   go-backend-demo
   

2. Ollama

   ollama run llama3.2
   

3. MCP Client

   go install github.com/mark3labs/mcphost@latest
   mcphost -m ollama:llama3.2 --config <.mcp.json_file_path>
   

Flow Diagram

🛠️ Need Help

I am working on improving tool definitions to enhance:
✅ Better error handling for more accurate responses
✅ LLM behavior control to ensure it relies only on API responses and does not use its own memory
✅ Preventing hallucinations and random data generation by enforcing strict data retrieval from APIs

If you have insights or suggestions on improving these aspects, please contribute by:

• Sharing your experience with similar implementations
• Suggesting modifications to tool definitions
• Providing feedback on current limitations

Your input will be invaluable in making this tool more reliable and effective! 🚀