Apidog MCP Server: The Bridge Between Your API Specifications and AI Coding Assistants

Software development is undergoing a profound transformation as artificial intelligence becomes increasingly integrated into coding workflows. Among the most significant innovations in this space is the Model Context Protocol (MCP) — a groundbreaking technology that creates intelligent connections between AI coding assistants and external knowledge sources.

Understanding MCP

MCP serves as a standardized communication channel that allows large language models (LLMs) to directly access, understand, and utilize specialized information from external applications. For developers, this means AI assistants can now draw upon contextual information beyond their training data, making them significantly more powerful and precise when tackling domain-specific tasks.

This protocol represents a fundamental shift in how AI assists with coding. Rather than relying solely on general knowledge, MCP-enabled AI assistants can tap into your specific documentation, codebase, and technical specifications. The result is a dramatically more accurate and contextually aware coding companion that understands the nuances of your particular project.

What is Apidog MCP Server?

Building upon this revolutionary MCP foundation, Apidog has developed the Apidog MCP Server — a specialized implementation designed specifically for API development workflows. This powerful tool creates a direct bridge between your API documentation and AI coding assistants, enabling what developers now call “vibe coding” — a flow state where you focus on creative problem-solving while your AI assistant handles implementation details with perfect knowledge of your API specifications.

Apidog MCP Server allows you to use your Apidog projects, public API doc sites published by Apidog, and any OpenAPI Specification (OAS) files as a data source for AI-powered IDEs like Cursor. This comprehensive integration means Agentic AI can directly access and work with your API documentation, speeding up development and making your workflow more efficient.

How Apidog MCP Server Works

Once the Apidog MCP Server is set up, it automatically reads and caches all API documentation data from your Apidog project or online project on your local machine. The AI can then retrieve and utilize this data seamlessly, creating an experience where your AI assistant can:

• Generate or modify code based on your exact API specifications
• Search through API documentation content to answer specific questions
• Create type-safe API clients that perfectly match your API structure
• Implement data validation logic based on your documented requirements
• Generate comprehensive test cases covering all documented scenarios

Using the server is remarkably straightforward. Simply instruct the AI on what you’d like to achieve with the API documentation. For example:

1. “Use MCP to fetch the API documentation and generate Java records for the Product schema and related schemas”
2. “Based on the API documentation, add the new fields to the Product DTO”
3. “Add comments for each field in the Product class based on the API documentation”
4. “Generate all the MVC code related to the endpoint /users according to the API documentation”

This direct connection eliminates one of the most significant friction points in API development: the constant context switching between documentation and implementation. Rather than manually referencing API specifications or explaining data models to your AI assistant, you can now rely on the assistant to access this information directly through the MCP server.

Transforming Development Workflows with Agentic AI

The integration of Apidog MCP Server with AI coding assistants creates a powerful synergy that fundamentally transforms how developers approach API-related tasks. This combination enables a truly agentic AI experience, where your coding assistant becomes an active participant in the development process with deep knowledge of your specific API design.

When working with Apidog MCP Server, your AI assistant can act as an autonomous agent that:

1. Analyzes requirements from your natural language instructions
2. Retrieves relevant API specifications directly from your documentation
3. Generates implementation code based on these specifications
4. Explains its reasoning and highlights important considerations
5. Suggests improvements or alternative approaches

This agentic capability dramatically reduces the cognitive load on developers. Instead of mentally juggling API specifications while writing implementation code, you can focus on higher-level design decisions and problem-solving while your AI assistant handles the details with precision.

Setting Up Apidog MCP Server: A Step-by-Step Guide

Getting started with Apidog MCP Server is straightforward. Follow these steps to connect your API documentation with your AI coding assistant:

Prerequisites

Before beginning the setup process, ensure you have:

• Node.js (version 18 or higher, preferably the latest LTS version)
• An IDE that supports MCP, such as Cursor or VSCode with the Cline plugin
• An Apidog account with access to your API project

Step 1: Generate an Access Token in Apidog

1. Open Apidog and log into your account
2. Hover over your profile picture at the top-right corner
3. Click “Account Settings > API Access Token”
4. Create a new API access token
5. Copy the generated token to a secure location — you’ll need this for configuration

Step 2: Locate Your Apidog Project ID

1. Open the desired project in Apidog
2. Click “Settings” in the left sidebar
3. Find the Project ID in the Basic Settings page
4. Copy this ID for use in your configuration

Step 3: Configure Your IDE for MCP Integration

1. Create or modify your MCP configuration file based on your IDE:

• For Cursor: Use either ~/.cursor/mcp.json (global) or .cursor/mcp.json (project-specific)
• For Cline: Open the Cline panel > MCP Server > Configure MCP Server

2. Add the following JSON configuration to your MCP file:

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

3. Replace the placeholder values:

• Substitute <project-id> with your actual Apidog Project ID
• Replace <access-token> with your Apidog API access token

For Windows users, if the above configuration doesn’t work, use this alternative:

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

Step 4: Verify and Test the Integration

1. Restart your IDE to ensure it loads the new MCP configuration
2. Test the integration by asking your AI assistant a question about your API, such as:

• “Use MCP to fetch the API documentation and list all available endpoints”
• “Based on the API documentation, what fields are in the User model?”

If the integration is working correctly, your AI assistant should be able to access and provide information from your API documentation without you having to manually reference or explain it.

Advanced Features and Tips

Working with OpenAPI Specifications

In addition to Apidog projects, Apidog MCP Server also has the ability to directly read Swagger or OpenAPI Specification (OAS) files. To use this feature:

1. Remove the --project-id=<project-id> parameter

2. Add the --oas=<oas-url-or-path> parameter, such as:

• npx apidog-mcp-server --oas=https://petstore.swagger.io/v2/swagger.json
• npx apidog-mcp-server --oas=~/data/petstore/swagger.json

Multiple Project Configuration

If you need to work with API documentation from several projects, simply add multiple MCP Server configurations to the configuration file. Each project should have its own unique project ID. For clarity, name each MCP Server following the format “xxx API Documentation”.

Security Best Practices

If your team syncs the MCP configuration file to a code repository, it is recommended to remove the line "APIDOG_ACCESS_TOKEN": "<access-token>" and instead, configure the APIDOG_ACCESS_TOKEN as an environment variable on each member's machine to prevent token leakage.

On-Premise Deployment

For users of the on-premise deployment, add the following parameter to the MCP configuration file in your IDE: "--apidog-api-base-url=<API address of the on-premise server, starting with http:// or https://>"

Additionally, ensure that your network can access www.npm.com properly.

Conclusion

Apidog MCP Server represents a significant advancement in how developers interact with API documentation and implement API-related functionality. By creating a direct connection between your API specifications and AI coding assistants, this powerful integration eliminates context switching, improves code quality, and dramatically accelerates development velocity.

By integrating Apidog MCP Server into your development workflow, you’re not just adopting a new tool — you’re embracing a fundamentally more efficient and enjoyable way to develop API-driven applications. The seamless connection between your documentation and AI assistant eliminates context switching, reduces errors, and allows you to maintain that coveted flow state where your best work happens.