Cursor + MCP Server: AI-Powered API Documentation as Code

Introduction: Bridging AI with Live API Specifications

In modern software development, APIs have become the lingua franca for system communication, while AI-assisted programming is fundamentally transforming our development workflows.

By integrating Apidog MCP Server with AI-powered IDEs like Cursor and VSCode + Cline, we've established a groundbreaking workflow: Enabling AI to directly read, comprehend, and leverage up-to-date API specifications - effectively implementing the "API Documentation as Code" paradigm.

This article details how I connected Apidog MCP Server with Cursor, demonstrating how this integration significantly enhances development efficiency, accuracy, and consistency.

What is Apidog MCP Server?

MCP (Model Context Protocol) is a protocol that enables AI systems to interact with external data sources. Think of it as a standardized endpoint for AI applications to connect various data sources and tools.

Apidog MCP Server allows developers to expose API documentation from Apidog projects as a data source for AI-enabled IDEs like Cursor. This direct access empowers AI assistants to:

• Generate or modify code based on API specs

• Search API documentation content

• Perform context-aware operations

The possibilities are limited only by your team's creativity.

Configuring Apidog MCP Server in Cursor

1. Prerequisites

• Node.js environment (≥ v18, LTS recommended)

• Latest Cursor version

• Accessible Apidog project

2. Obtaining Credentials

2.1 Get Apidog Access Token

• Open Apidog application

• Hover over profile avatar → "Account Settings → API Access Token"

• Create and save new API access token

2.2 Get Apidog Project ID

• Open target Apidog project

• Click "Settings" in left sidebar

• Copy Project ID from "Basic Settings"

3. Configuring MCP Settings

Two configuration approaches: Global (recommended) or Project-specific.

Method 1: Global Configuration

1. In Cursor: Click settings icon → MCP → "+ Add new global MCP server"

2. In the opened mcp.json file, add the following configuration, replacing <access-token> and <project-id> with your actual values.

{
  "mcpServers": {
    "API specification": {
      "command": "npx",
      "args": [
        "-y",
        "apidog-mcp-server@latest",
        "--project-id=<project-id>"
      ],
      "env": {
        "APIDOG_ACCESS_TOKEN": "<access-token>"
      }
    }
  }
}

Windows users may need:

{
  "mcpServers": {
    "API specification": {
      "command": "cmd",
      "args": [
        "/c",
        "npx",
        "-y",
        "apidog-mcp-server@latest",
        "--project-id=<project-id>"
      ],
      "env": {
        "APIDOG_ACCESS_TOKEN": "<access-token>"
      }
    }
  }
}

Method 2: Project-specific Configuration

• Create .cursor folder in project root

• Add mcp.json with same configuration

• Use the same configuration as above

4. Verifying MCP Connection

Test with AI prompt in Agent mode:

"Fetch API documentation via MCP and list the number of endpoints"

Successful API information retrieval confirms proper setup.

Real-World Use Cases: Supercharging AI + API Development

Case 1: Rapid Model Generation

For an e-commerce app with product APIs:

1. Create new Cursor project

2. Prompt:

"Fetch product APIs via MCP and generate complete TypeScript endpoints with service classes"

AI will:

• Analyze API structure

• Generate compliant code

Sample output:

Case 2: Documentation-Code Synchronization

After adding discountPrice field in Apidog:

Prompt:

"Refresh MCP cache and update Product endpoint with new fields"

AI updates:

discountPrice?: number; // New field

Case 3: CRUD Generation

Prompt:

"Generate complete Spring Boot MVC code for user management per API docs"

AI produces compliant controllers/services/repositories.

Case 4: API Documentation Q&A

Prompt:

"Explain payment-related endpoints and their workflow"

AI analyzes and explains payment flow.

Case 5: Automated Test Generation

Prompt:

"Generate Jest tests for product creation endpoint using MCP docs"

AI creates test cases covering edge conditions.

Pro Tips & Best Practices

Multi-Project Management

Configure multiple MCP servers:

{
  "mcpServers": {
    "Mall API Documentation": {
      "command": "npx",
      "args": [
        "-y",
        "apidog-mcp-server@latest",
        "--project=123456"
      ],
      "env": {
        "APIDOG_ACCESS_TOKEN": "<access-token>"
      }
    },
    "CRM API Documentation": {
      "command": "npx",
      "args": [
        "-y",
        "apidog-mcp-server@latest",
        "--project=123456"
      ],
      "env": {
        "APIDOG_ACCESS_TOKEN": "<access-token>"
      }
    }
  }
}

Specify context:

"Use CRM API Documentation to generate order management code"

Security Practices

To prevent exposing the Access Token in team collaboration:

• Remove the "APIDOG_ACCESS_TOKEN": "<access-token>" line from configuration files.

• Set the APIDOG_ACCESS_TOKEN environment variable on each developer's system.

Tips for Improving AI Response Quality

• Specify the required documentation section: e.g., "Please refer to the API documentation for the user module."

• Define coding style and conventions: e.g., "Generate code following our team's TypeScript naming conventions."

• Request comments: e.g., "When generating code, add detailed JSDoc comments for each method."

• Break tasks into steps: For complex tasks, first ask the AI to outline a plan, then execute step by step.

Conclusion

The Apidog MCP Server + Cursor integration delivers an unprecedented API development experience by enabling:

1. Dramatically reduced spec-to-code time

2. Fewer human errors

3. Faster iteration cycles

4. Enhanced code quality

As Apidog MCP Server evolves, developers can expect more powerful API workflow optimizations. This AI-API synergy represents a significant productivity leap for both frontend and backend developers.