list_codes

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

No annotations are provided (readOnlyHint, destructiveHint, etc.), so the description carries the disclosure burden. The verb 'list' implies a read-only operation, which is helpful, but there's no mention of external network behavior, potential rate limits, or whether results are cached—reasonable context for a tool that queries Eurostat data.

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?

Two sentences, with the key information front-loaded and a minimal working example. Every word earns its place.

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 the return value is documented. The description covers the essentials for a simple listing tool. The main gap is that it doesn't mention edge cases, such as what happens for a dimension with many values or whether the result is paginated/limited, but the example is solid enough to prevent basic misuse.

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 example `contains="fr"` indirectly clarifies the `contains` parameter, and using `"nama_10_gdp"` for `dataset` and `"geo"` for `dimension` gives meaning to all three params. However, the description doesn't explicitly say datasets are Eurostat datasets or that dimensions are named fields like 'geo', 'time', 'unit', so an agent must infer this from the example.

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 identifies the operation: list codes of a dimension with optional substring filtering. The concrete example with "nama_10_gdp" and "geo" makes the tool's purpose immediately understandable, even without documentation of the 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?

The usage context is implied through the dimension and filter semantics, and the example shows how to use it. However, there's no explicit guidance on when to choose this over alternatives like query_data or search_datasets, nor does it clarify what a dimension is in the Eurostat context.

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