List Census International Trade Datasets

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

Annotations declare readOnly, idempotent, and non-destructive behavior, so the description's burden is lower. It adds valuable context that each dataset covers the same underlying monthly trade data (2010-present) and outlines the return structure (short code, full name, description, level of detail, accepted parameters). This goes beyond the annotations, though it doesn't discuss any side effects or potential pitfalls (none likely for a list operation).

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 front-loaded with the purpose, then flows into usage guidance, return format, and examples. Every sentence earns its place; the use/don't-use examples are particularly informative and not redundant. It's appropriately sized for a tool that needs to guide dataset selection.

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 zero-parameter tool with no output schema, the description fully covers what the agent needs to know: what the list contains, the structure of each entry, and how to use the results with sibling tools (e.g., the short code is used as the 'dataset' parameter in census_trade_query_exports/imports). It also gives helpful context about the commodity classifications and geography options.

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 tool has zero parameters, and the schema coverage is 100% (no properties). The description explicitly notes 'Args: none,' which is sufficient. Per the calibration, 0 params baseline is 4; there is nothing more to add.

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 ('List'), a concrete resource ('the 9 commodity classification / geography datasets available in the Census International Trade Data API'), and scope ('for both exports and imports'). This clearly distinguishes it from siblings that query specific data, retrieve variables, or compute balances.

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?

Provides explicit when-to-use guidance ('Use this first when you're not sure which dataset to query'), concrete example use cases ('What trade datasets are available?'), and an explicit exclusion ('Don't use when: You already know the dataset code you need - go straight to census_trade_query_exports/imports'). This is exemplary usage guidance.

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