So, Saw an interesting finding shared by the team at Cala on LinkedIn that just shipped an MCP server with three ways for agents to access their knowledge graph: natural language queries, a structured query language, and direct entity/relationship traversal.
They expected agents to default to natural language. That's the whole point of LLMs, right?
Nope. Most agents abandoned natural language within minutes and switched to structured queries and graph traversal on their own. No prompting, no nudging.
This actually makes sense when you think about it. LLMs aren't explicitly trained to be "efficient", they're trained to be correct (via RLHF). But correctness makes them behave efficiently as a side effect. They learn to take the shortest reliable path to a solution. Natural language is a lossy interface as it adds an interpretation layer the agent doesn't need when structured queries give deterministic results.
So when given three doors, they picked the one that minimized uncertainty, not the one that felt most "natural."
A few questions this raises:
- Are we over-indexing on natural language interfaces for agent tooling?
- Should MCP servers prioritize structured/graph-based access patterns over NL by default?
- If agents prefer deterministic paths, does that change how we think about tool design?
Curious what others are seeing. Anyone building agent tooling noticed similar patterns?
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