cuba_juez

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full behavioral burden. It usefully discloses two side effects: that this is an LLM-backed (costly) operation and that verdicts are cached in brain_judgments. It does not state permissions required, cost bounds, or whether cached verdicts are reused/overwritten, leaving meaningful gaps for a mutating, LLM-invoking tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two tight sentences with no filler, and the core purpose and the similarity band are front-loaded. The phrasing is dense but every clause contributes information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

There is no output schema and no annotations, so the description should do more of the explanatory work. It names the cache destination but never describes what a verdict returns or its shape, and the two actions are only clarified in the schema. Adequate to route a call but incomplete for a judge tool without structured output.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all five parameters including the action enum and max_pairs cost control. The description adds no syntax or format detail beyond that. Baseline 3 is appropriate when the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource: an LLM-judge that resolves semantically-conflicting observations in a defined cosine-similarity band. It clearly positions itself against heuristics ('where heuristics miss vocabulary-different conflicts'), which aids differentiation. It stops short of naming any actual sibling tool (e.g., memory_juez or cuba_contradiccion), so selection vs. siblings remains inferential.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives a concrete triggering condition: use when observations fall in the ambiguous 0.6-0.8 band and vocabulary-different conflicts would evade heuristics. That is clear when-to-use context. It offers no explicit when-not-to-use or pointer to an alternative tool, so it does not reach the top of the scale.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.