list_variables

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

No annotations are provided, so the description must carry the burden. It only states it 'returns a list of variables with their labels' but does not disclose read-only nature, potential side effects, or any behavioral traits. This is insufficient for a 0-annotation 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?

The description is concise with a clear first line stating purpose, followed by a structured Args section. Every sentence adds value, no fluff.

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?

The tool has an output schema, so return details are not required. The description covers key aspects: purpose, parameters, and return type. However, it lacks usage context (e.g., when to choose this over 'variable_detail') making it slightly incomplete.

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 coverage is 0% but the description compensates by detailing the 'dataset' options ('entrevistado, hogar, inclusion_financiera') and explaining that 'section' and 'search' are optional filters. This adds meaning beyond the schema's type and default information.

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 'List available variables in the survey' with specific verb and resource. It distinguishes from siblings like 'variable_detail' which handles individual variables, and 'describe_survey' which is broader.

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?

The description explains what the tool does and lists arguments, but provides no guidance on when to use it versus alternatives or when not to use it. It does not give explicit context for selection among sibling tools.

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