Second that. A lot of our use cases are "remote tooling", i.e. calling APIs. Implementing an MCP server to wrap APIs seems very complex - both in terms of implementation and infrastructure.

We have found GraphQL to be a great "semantic" interface for API tooling definitions since GraphQL schema allows for descriptions in the spec and is very humanly readable. For "data-heavy" AI use cases, the flexibility of GraphQL is nice so you can expose different levels of "data-depth" which is very useful in controlling cost (i.e. context window) and performance of LLM apps.

In case anybody else wants to call GraphQL APIs as tools in their chatbot/agents/LLM apps, we open sourced a library for the boilerplate code: https://github.com/DataSQRL/acorn.js