AI assistants like ChatGPT, Claude, and Cursor are rapidly reshaping how developers interact with software. Whether debugging code, writing documentation, or analyzing data, these tools are becoming indispensable. However, AI tools need structured access to your APIs to be truly useful. Connecting them has been harder than it should be.

Anthropic's new Model Context Protocol (MCP) is an open standard that solves the problem of exposing APIs to LLMs in a portable and predictable way. In this article, we'll break down what MCP is, why it matters, and how you can generate a ready-to-use MCP server from your OpenAPI spec with just one command using liblab.

What is MCP and Why It Matters

MCP is a lightweight, machine-readable specification that bridges the gap between traditional APIs and AI agents.

Think of MCP as an adapter. It translates your API into a format that AI tools can understand and use. This approach opens up the ability for anyone to use natural language to interact with your API.

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You can visit the MCP Concept page for more details about how MCP works.

Here's how it works:

1. You generate an OpenAPI spec from your API. We have guides on how to do this.
2. You use liblab to generate an MCP server that conforms to the MCP standard.
3. You host the MCP server and make its endpoint URL available to the AI tool.
4. Supported AI tools like Claude, Cursor, Gemini, and Windsurf can then connect to your MCP server via their UI or plugin interfaces, discover your API structure, and start passing user requests to your endpoints in real time.

This matters because most APIs weren't designed with LLMs in mind. They rely on humans reading docs and creating workflows to use APIs. But AI agents need structured metadata, clear input/output formats, and callable endpoints. By standardizing this layer, MCP turns your API into an AI-native interface.

Benefits of Using MCP Servers

Using MCP servers isn't just about keeping up with AI trends — it's about unlocking powerful advantages for your team and your users:

Seamless AI Integration

Instead of hardcoding specific behaviors into your AI tool, MCP allows the model to understand and use your API dynamically. That means:

• Tools like Claude, and Gemini can call your endpoints.
• Agents in Cursor, and Windsurf can explore your API and create requests interactively.
• No need to build custom SDKs or plugins for each tool.

Consistent, Spec-Driven Behavior

MCP servers are generated from OpenAPI specs. That gives you:

• Consistency across tools and environments.
• Reduced maintenance since MCPs are generated based on your API spec.
• Predictable outputs that models can reason over.

Secure and Controlled Access

Because MCP servers follow your existing API security patterns (e.g., headers, keys), you maintain full control over who can access what — with no risk of LLMs running wild in your infrastructure. At the same time, AI tools such as Cursor will prompt users for permission before executing any requests, ensuring that human oversight remains part of the workflow.

Portable and Deployable

An MCP server is a small, self-contained service. You can deploy it alongside your API stack or run it locally. It doesn't modify your existing API — it simply mirrors it in an AI-accessible form.

How to Generate an MCP Server with liblab

To generate an MCP server:

1. Start with an OpenAPI Spec: All you need is the OpenAPI spec for your API, either as a local file or hosted at a public URL.
2. Install liblab: If you haven't already, install liblab using npm install -g liblab.
3. Run liblab init -s path/to/your/openapi.json: The CLI walks you through selecting the project name and enables MCP server generation.
4. Create the MCP Project: liblab enables you to generate both your SDKs and an AI-ready MCP server. Just answer "Yes" when prompted!

liblab init -s path/or/url/to/your/openapi.json
Let's build your SDK with a few simple steps!
? Select languages for SDK generation: Python
? Please enter your project name: my-mcp-server 
? Select the project licence: MIT
? Generate Model Context Protocol (MCP) server to enable SDK integration with AI models? Yes
Successfully created liblab.config.json file.

Your SDKs are being generated. Visit the liblab portal to view more details on your build(s).
✓ Python succeeded
✓ MCP succeeded
✨ Successfully generated SDKs for MCP, PYTHON! ♡ You can find them inside: ./output

5. Build the MCP Server: After generating the MCP, run npm run setup in the output/mcp directory to install the dependencies and build the MCP server. At this point, your MCP server is ready to be used by any AI tool.
6. Connect to AI Assistants: Tools like Cursor, Claude, Gemini, and Windsurf can load your MCP config and let you interact with your API directly from the chat. You don't need to run the MCP server manually — the AI tools will start it automatically when needed. Refer to their documentation for instructions on how to connect your MCP server.

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Check our guide on how to Generate an MCP Server with liblab for more details.

The entire process takes just a few minutes and lets developers make APIs usable by AI with no extra boilerplate.

Make Your API AI-Native in Minutes

As AI agents become more capable, the question isn't whether they'll need to talk to your APIs — it's how easily they can do it.

MCP provides a clear, portable way to make your APIs AI-accessible. And with liblab's MCP generation feature, you don't need to reinvent your infrastructure or write glue code to connect with tools like Cursor or Claude.

Try It Yourself

Ready to give it a spin? Visit the liblab MCP documentation to learn more aboute MCPs or check the guide on how to Generate an MCP Server with liblab to generate your first MCP server today.