From IDE to AI: How ReadMe bridges APIs to LLMs with MCP

ReadMe powers APIs, and APIs power AI, so of course we're psyched about Model Context Protocol (MCP), a new standard that bridges AI agents and developer tools. It's right in line with our mission to make APIs easier to use with an intuitive developer experience.

We’ve launched our own MCP server and made it possible for API creators to do the same. Now, ReadMe customers can edit docs directly from AI code editors. Customers can also launch their own MCP servers, making APIs instantly more accessible and understandable to AI tools to meet developers where they are.

What Is MCP and Why it Matters for APIs

Model Context Protocol (MCP), introduced by Anthropic in late 2023, standardizes how tools and data sources connect to AI models.

It enables:

• Large-language modles (LLMs) to understand and use data from APIs and documentation
• Function calling, not just text generation
• AI agents to act on information, not just understand it

In short, MCP gives models both the knowledge and the ability to complete tasks.

One Way to Think About MCP: A Well-Organized Toolbox

I've been covered in sheetrock dust from our home improvement projects lately, which might explain why all I can think about when I hear ''tooling'' is the screwdriver on my desk from hanging shelves over the weekend.

MCP servers are APIs or services that expose their data in an MCP-compatible format, making it easier for AI to access and use APIs. I think of them as a toolbox you hand to an AI agent, filled with power drills, screws, and instructions (APIs, data sources, and docs). The toolbox tells the AI how each drill works and remembers which screws need a flathead and which need a Phillips.

Now, the AI doesn't just know which bit to use. It can pick up the power drill, slot in the right bit, and screw it in. It uses the right tool for the right job and actually gets the task done.

Generating MCP Servers for Your Developers

Developers can use MCP servers to not only learn about APIs, but also to take action and build even faster. With ReadMe, an MCP server is automatically provided out of the box for every project. All you have to do is enable MCP to connect your documentation and API to AI tools. You can share your MCP URL with your developers, and they can connect their AI assistants and tools directly to your API.

This means that API creators can enable developers to build outside of docs with confidence that docs and APIs remain the source of truth. Rather than context-switching between documentation, IDEs, and testing tools, developers can work directly within their AI code editor while having instant access to API documentation.

Use ReadMe's MCP Server to Improve API Docs with AI

With ReadMe's MCP server, you can do everything you normally would in ReadMe, like adding and editing pages, directly through our API.

Our favorite workflow is:

1. Ask to find underperforming pages by using the Metrics API to identify docs with low page views.
2. Ask for suggestions on how to improve each page's content, using your best performing docs as a reference.
3. Edit those pages directly from IDEs like VS Code or Cursor, with ReadMe's MCP server.

To get started, you'll need your ReadMe API key. Once you have that, you can plug it into Cursor and start improving your docs.

In addition to Cursor, we also support IDE integrations with VS Code, Claude, Windsurf, and Gemini CLI.

Getting Started with MCP

Choose how you’d like to start working with MCP:

1. Auto-Generate Your Own MCP Server: Simply enable MCP to connect your API documentation to AI tools.

2. Use ReadMe's MCP Server: You can do everything you normally would in ReadMe, like adding and editing pages, directly through our API.