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

With no annotations, the description carries the burden of disclosing behavior. It does disclose the core behavior (NL-to-SQL conversion) and the pay-per-use cost model, but it does not mention output format, error behavior, or whether SQL is executed.

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 a single concise, front-loaded sentence covering function, input, and pricing. There is no filler, redundancy, or unnecessary detail.

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 one-parameter conversion tool, the essential usage information is present: what to input and what transformation happens. However, the return value/format is only implied by 'a SQL', and no distinction is made from the sibling 'text-to-sql' tool, leaving some context missing.

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 only describes the parameter as 'service input', while the description adds 'input=petición en lenguaje natural', giving the agent concrete guidance on what content to provide. This is a genuine semantic addition beyond the generic schema text.

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: it converts a natural-language request to SQL. It is clear, but it does not differentiate itself from the closely related sibling tool 'text-to-sql'.

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?

There is no guidance about when to use this tool versus alternatives such as 'text-to-sql'. The only usage-like hint is what to pass in the input parameter, not when this tool should be preferred.

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