An API (Application Programming Interface) is a set of rules and protocols that allows different software applications to communicate and interact with each other.
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You’ve built an MCP server. It accesses data, performs actions, and works perfectly in your local development environment.
Now what?
To make your tools truly useful, they need to be accessible—whether by your team, your organization, or the global community of AI developers. This guide covers how to take your MCP server from localhost to production, and how to register it so it can be discovered.
For years, OpenAPI (OAS) has been the cornerstone of how we describe, document, and integrate APIs. It standardized how systems talk to each other — and in many ways, it made the modern internet possible.
So when the Model Context Protocol (MCP) appeared, some developers were skeptical. "Another spec?" they asked. "Why do we need this when we already have OAS?"
🚨 "OpenAPI Specification (OAS) v4 is out" - That I wish, this is the kind of headline I would expect to see soon, because OAS can easily be extended to enable RESTful APIs work seamlessly with AI.
By the end of this article, you'll know how to let any LLM call your REST tools automatically using OAS.
