Swagger MCP

Note: This project is a fork of the original Swagger MCP, intended to be published to npm and enable easy usage via npx @tigawanna/swagger-mcp.

An MCP server that connects to a Swagger specification and helps an AI to build all the required models to generate a MCP server for that service.

Features

• Downloads a Swagger specification and stores it locally for faster reference.
• Returns a list of all the endpoints and their HTTP Methods and descriptions
• Returns a list of all the models
• Returns a model
• Returns service to connect to the end point
• Returns MCP function definitions
• Generates complete MCP tool definitions with full schema information
• Includes AI-specific instructions in tool descriptions

Prerequisites

• Node.js (v14 or higher)
• npm or yarn

Installation

Quick Start (via npx)

npx @tigawanna/swagger-mcp

Global Installation

npm install -g @tigawanna/swagger-mcp

From Source

1. Clone the repository:

git clone https://github.com/tigawanna/swagger-mcp.git
cd swagger-mcp

2. Install dependencies:

npm install

3. Create a .env file based on the .env.example file:

cp .env.example .env

4. Update the .env file.

Configuration

Edit the .env file to configure the application:

• PORT: The port on which the server will run (default: 3000)
• NODE_ENV: The environment (development, production, test)
• LOG_LEVEL: Logging level (info, error, debug)

Usage

Building the application

Build the application:

npm run build

This will compile the TypeScript code ready to be used as an MCP Server

Running as an MCP Server

To run as an MCP server for integration with Cursor and other applications:

node build/index.js

Using the MCP Inspector

To run the MCP inspector for debugging:

npm run inspector

Adding to Cursor

To add this MCP server to Cursor, you can use either the deployed version or a local build:

Option 1: Using the Deployed Version (Recommended)

Add the following configuration to your MCP config file (typically ~/.codeium/windsurf/mcp_config.json or Claude's config):

{
  "mcpServers": {
    "swagger-mcp": {
      "command": "npx",
      "args": ["@tigawanna/swagger-mcp"]
    }
  }
}

This method uses the published npm package and doesn't require a local build.

Adding Environment Variables

You can pass environment variables inline in the MCP configuration:

{
  "mcpServers": {
    "swagger-mcp": {
      "command": "npx",
      "args": ["@tigawanna/swagger-mcp"],
      "env": {
        "PORT": "3000",
        "NODE_ENV": "development",
        "LOG_LEVEL": "info",
        "TEAMWORK_DOMAIN": "your-domain",
        "TEAMWORK_USERNAME": "your_username_here",
        "TEAMWORK_PASSWORD": "your_password_here"
      }
    }
  }
}

Available Environment Variables:

• PORT: The port on which the server will run (default: 3000)
• NODE_ENV: The environment (development, production, test)
• LOG_LEVEL: Logging level (info, error, debug)
• TEAMWORK_DOMAIN: Your Teamwork domain
• TEAMWORK_USERNAME: Your Teamwork username
• TEAMWORK_PASSWORD: Your Teamwork password

Option 2: Using a Local Build

1. Open Cursor Settings > Features > MCP
2. Click "+ Add New MCP Server"
3. Enter a name for the server (e.g., "Swagger MCP")
4. Select "stdio" as the transport type
5. Enter the command to run the server: node path/to/swagger-mcp/build/index.js and then if needed, add the command line arguments as mentioned above.
6. Click "Add"

The Swagger MCP tools will now be available to the Cursor Agent in Composer.

Available Swagger MCP Tools

The following tools are available through the MCP server:

• getSwaggerDefinition: Downloads a Swagger definition from a URL
• listEndpoints: Lists all endpoints from the Swagger definition
• listEndpointModels: Lists all models used by a specific endpoint
• generateModelCode: Generates TypeScript code for a model
• generateEndpointToolCode: Generates TypeScript code for an MCP tool definition

Available Swagger MCP Prompts

The server also provides MCP prompts that guide AI assistants through common workflows:

• add-endpoint: A step-by-step guide for adding a new endpoint using the Swagger MCP tools

To use a prompt, clients can make a prompts/get request with the prompt name and optional arguments:

{
  "method": "prompts/get",
  "params": {
    "name": "add-endpoint",
    "arguments": {
      "swaggerUrl": "https://petstore.swagger.io/v2/swagger.json",
      "endpointPath": "/pets/{id}",
      "httpMethod": "GET"
    }
  }
}

The prompt will return a series of messages that guide the AI assistant through the exact process required to add a new endpoint.

Setting Up Your New Project

First ask the agent to get the Swagger file, make sure you give it the URL for the swagger file, or at least a way to find it for you, this will download the file and save it locally with a hashed filename, this filename will automatically be added to a .swagger-mcp settings file in the root of your current solution.

Auto generated .swagger-mcp config file

SWAGGER_FILENAME = TheFilenameOfTheLocallyStoredSwaggerFile

This simple configuration file associates your current project with a specific Swagger API, we may use it to store more details in the future.

Once configured, the MCP will be able to find your Swagger definition and associate it with your current solution, reducing the number of API calls needed to get the project and tasks related to the solution you are working on.

Improved MCP Tool Code Generator

The MCP tool code generator has been enhanced to provide more complete and usable tool definitions:

Key Improvements

1. Complete Schema Information: The generator now includes full schema information for all models, including nested objects, directly in the inputSchema.

2. Better Parameter Naming: Parameter names are now more semantic and avoid problematic characters like dots (e.g., taskRequest instead of task.Request).

3. Semantic Tool Names: Tool names are now more descriptive and follow consistent naming conventions based on the HTTP method and resource path.

4. Support for YAML Swagger Files: The generator now supports both JSON and YAML Swagger definition files.

5. Improved Documentation: Generated tool definitions include comprehensive descriptions for all parameters and properties.

6. No External Dependencies: The generated code doesn't require importing external model files, making it more self-contained and easier to use.

7. AI-Specific Instructions: Tool descriptions now include special instructions for AI agents, helping them understand how to use the tools effectively.

Example Usage

To generate an MCP tool definition for an endpoint:

import generateEndpointToolCode from './services/generateEndpointToolCode.js';

const toolCode = await generateEndpointToolCode({
  path: '/pets',
  method: 'POST',
  swaggerFilePath: './petstore.json',
  singularizeResourceNames: true
});

console.log(toolCode);

This will generate a complete MCP tool definition with full schema information for the POST /pets endpoint.

License

This project is licensed under the MIT License - see the LICENSE file for details.

MCP Prompts for AI Assistants

To help AI assistants use the Swagger MCP tools effectively, we've created a collection of prompts that guide them through common tasks. These prompts provide step-by-step instructions for processes like adding new endpoints, using generated models, and more.

Check out the PROMPTS.md file for the full collection of prompts.

Example use case: When asking an AI assistant to add a new endpoint to your project, you can reference the "Adding a New Endpoint" prompt to ensure the assistant follows the correct process in the right order.