eurostat_get_dataset_structure

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

With no annotations, the description carries the full burden of behavioral disclosure, but it stays mostly at surface level. It discloses what the tool returns (a Dict of dimensions to value lists), which is useful, but it does not describe potential failure modes (e.g., what happens with an invalid dataset_code), network/API behavior, pagination, rate limits, or whether the call is safe/idempotent. For a read-only metadata lookup, this is acceptable but not thorough.

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 compact and front-loaded: first sentence states the action and resource, followed by an Args section and a Returns section. Every sentence serves a purpose and there is no filler. The Docstring-style format is clean and immediately scannable for an agent.

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 there is an output schema, the return type is partly covered by the schema, but the description also adds the return shape. The tool is a simple 1-parameter getter, so it does not need extensive context. However, it lacks information about error handling (e.g., what happens for an unknown dataset_code) and does not give example codes, which would make the tool fully self-contained.

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%, so the description must compensate for the fact that 'dataset_code' is only defined as a string in the schema. The description does state 'Eurostat dataset code' and its use in the Args section, which adds minimal meaning beyond the schema but the description is still thin. It doesn't provide examples of valid codes or format guidance, so a new agent would still be uncertain what to pass. The description adds a little value but does not fully compensate for 0% schema coverage.

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 ('Get') and resource ('dimension info for a Eurostat dataset'), clearly distinguishing it from siblings like insee_get_data or insee_get_indicator_metadata, which are different statistical agencies and operations. The mention of return value ('Dict mapping dimension names to lists of possible values') adds clarity. However, it does not explicitly name sibling tools for differentiation, and the purpose could be slightly sharper about what 'dimension info' means in Eurostat terms.

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 implies this is the tool to use when an agent needs to know the dimensions (and their allowed values) of a Eurostat dataset before making a data request. There is no explicit when-to-use/when-not-to-use guidance or mention of alternatives (e.g., insee_get_indicator_metadata for INSEE). The context is clear enough for a knowledgeable agent, but lacks explicit routing instructions to distinguish it from siblings.

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