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0 pointsmbroecheler0y ago0 comments

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

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0x00811mo ago

Why does it require you to build a server to wrap APIs? I think if the api uses HTTP you could just expose a SSE endpoint for the mcp client to use?

_pdp_0y ago

Oh wow. Amazing. I did not think of that. I am not fan of GraphQL but you might be onto something here. I have not checked the code and perhaps this is not the right channel for this but my read is that this library allows any generic GraphQL server to exposed in this sort of way?

mbroechelerOP0y ago

Exactly, any generic GraphQL server can be turned into a set of LLM tools with minimal overhead and complexity.

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