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

No annotations are provided, so the description carries the transparency burden. It discloses a pay-per-use cost and that the operation is inference-like (detect/classify/extract), but it does not mention return behavior, side effects, or any required setup/auth beyond the pricing note.

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?

The description is very short and front-loaded with the core function. The bracket note about cost is a pragmatic addition, though the formatting is somewhat telegraphic and mixed English/Spanish.

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?

For a single-parameter inference tool, the description covers the basic purpose and payment requirement. However, without an output schema or any explanation of expected result format, agents may need to discover the resolution of extracted parameters by trial.

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?

The schema describes the single required parameter as 'mensaje a interpretar', and the description repeats it as 'input=message'. Since schema coverage is 100%, the description does not add meaningful information about the parameter beyond what the schema already provides.

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 clearly states the tool's function: detect message intent by classifying intent and extracting parameters. This distinguishes it from broad tools like classify-topic or sentiment, though it does not explicitly name or differentiate itself from sibling tools.

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?

No guidance is given about when this tool should be used instead of alternatives like classify-topic, entities, or inference. The phrase 'input=message' is a parameter note, not usage direction.

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