It is called graphql.
The agent writes a query and executes it. If the agent does not know how to do particular type of query then it can use graphql introspection. The agent only receives the minimal amount of data as per the graphql query saving valuable tokens.
It works better!
Not only we don't need to load 50+ tools (our entire SDK) but it also solves the N+1 problem when using traditional REST APIs. Also, you don't need to fall back to write code especially for query and mutations. But if you need to do that, the SDK is always available following graphql typed schema - which helps agents write better code!
While I was never a big fan of graphql before, considering the state of MCP, I strongly believe it is one of the best technologies for AI agents.
I wrote more about this here if you are interested: https://chatbotkit.com/reflections/why-graphql-beats-mcp-for...
2 years ago I gave a talk on Vector DB's and LLM use.
https://www.youtube.com/watch?v=U_g06VqdKUc
TLDR but it shows how you could teach an LLM your GraphQL query language to let it selectively load context into what were very small context windows at the time.
After that the MCP specification came out. Which from my vantage point is a poor and half implemented version of what GraphQL already is.
For those that don't know its also built upon OTP, the erlang vm that makes concurrency and queues a trivial problem in my opinion.
Absolutely wonderful ecosystem.
I've been wanting to make Gleam my primary language, but I fear LLMs have frozen programming language advancement and adoption for anything past 2021.
But I am hopeful that Gleam has slid just under the closing door and LLMs will get up to speed on it fast.