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Thoughts on the Model Context Protocol

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Let's say you want to integrate an external API into your application. You look up the documentation, understand how the API works, and then write code against it. This happens at design-time — your application is now permanently coupled to that specific API. Obviously! But LLMs could understand and interact with arbitrary endpoints. This suggests we could shift the burden of understanding an API from the developer to the LLM, or in other words, from design-time to runtime. So why can't we connect AI applications to arbitrary software while they're running? Unfortunately, there is no consensus yet on how applications should collect API metadata, or conversely, how API providers should expose them. Simply speaking, APIs expect the caller to already know how they work. And this is starting to become a limitation. The missing piece seems to be some kind of open meta-protocol that makes the API metadata and semantics available to the caller at runtime. The Model Context Protocol (MCP) by A

Thoughts on MCP Let’s say you want to integrate an external API into your application. You look up the documentation, understand how the API works, and then write code against it. This happens at design-time — your application is now permanently coupled to that specific API. Obviously! But LLMs could understand and interact with arbitrary endpoints. This suggests we could shift the burden of understanding an API from the developer to the LLM, or in other words, from design-time to runtime . So why can’t we connect AI applications to arbitrary software while they’re running? Unfortunately, ther

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