Cala's team shared a finding on LinkedIn that's worth sitting with. They shipped an MCP server with three access patterns for their knowledge graph: natural language queries, a structured query language, and direct entity/relationship traversal.

They expected agents to default to natural language. Instead, most agents switched to structured queries and graph traversal on their own. No prompting, no nudging.

The obvious explanation is "agents prefer efficiency." I don't think that's quite right. What they prefer is determinism.

A natural language query introduces two interpretation layers: the agent generates a query in prose, a system interprets that prose, then returns a result the agent has to parse. At no point can the agent verify the query was understood correctly. With a structured query, the contract is explicit. The agent knows exactly what it asked for and can verify what it got back.

This isn't an emergent preference for efficiency. It's tool-use chain-of-thought doing what it's supposed to do: picking the path where the agent can most reliably confirm it got the right answer before moving to the next step.

A few implications if this holds up:

- NL-first tool interfaces might be optimizing for the wrong user. The human operator wants NL. The agent doesn't. - MCP servers that only expose NL endpoints are forcing agents through a non-deterministic bottleneck they'd avoid if given the choice. - Tool design for agents should probably default to structured access with NL as a fallback, not the other way around.