Get Valid Variables for a Trade Dataset

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, establishing a safe read operation. The description adds valuable context beyond this: it notes the Census API rejects unknown variable names with a 400 error, and that valid variables vary by dataset (e.g., SITC only on the sitc dataset). This failure-mode information helps the agent anticipate user needs and error conditions without contradicting any annotations.

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 well-organized into distinct sections: purpose, usage, args, returns, and examples. Every sentence serves a purpose, the opening is front-loaded with the core verb and resource, and the examples provide concrete use cases. Despite being longer than average, it avoids redundancy and is scannable.

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?

Given that there is no output schema, the description appropriately details the return structure ('For each variable - its name, human-readable label, whether it's required, and its type'). It also covers the full lifecycle: when to use, how to invoke, what to expect, and when not to use. The tool is fully self-contained and contextually complete for an agent navigating a complex API.

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%, with each parameter (direction, dataset, response_format) having a full description and enum values. The description's Args section somewhat duplicates this information, but it adds a small amount of context, such as the note to 'see census_trade_list_datasets for the full list' and the default value for response_format. Since the schema already carries the heavy load, the description adds marginal value, keeping this at the baseline 3.

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 opens with a specific verb+resource: 'List every valid Census API variable (field) name for a specific dataset + trade direction.' This clearly distinguishes it from sibling tools like census_trade_query_exports/imports and census_trade_list_datasets. The explicit mention of listing fields rather than querying data or listing datasets removes any ambiguity.

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 explicitly instructs when to use the tool ('Use this before calling census_trade_query_exports/imports when you're unsure which variable names are valid'), and provides a clear 'Don't use when' case with a named alternative (use census_trade_list_datasets instead). This exceeds typical guidance by giving both positive and negative usage scenarios.

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