get_dataset_info

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

With no annotations, the description carries the burden. It explicitly lists what the tool returns ('metadata, dimension structures, time ranges, sample values') and the word 'Fetch' implies a read-only operation. It does not detail response format or pagination, but the output schema covers return structure, making this adequately transparent.

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?

A single, front-loaded sentence that conveys the exact purpose without waste. Every word adds value and no extraneous details are included.

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 is simple (one param) and has a full output schema, so the description need not explain return values. The description provides sufficient context for an agent to understand the tool's scope, though it could benefit from a hint about when to use it relative to siblings.

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 input schema fully describes the single parameter with a clear description and examples (e.g., 'nama_10_gdp'). The description adds no parameter-specific information, so the baseline of 3 applies per the schema_description_coverage of 100%.

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 uses a specific verb ('Fetch') and clearly identifies the resource ('metadata, dimension structures, time ranges, and sample values for a Eurostat dataset'). This distinguishes it from siblings like query_dataset (actual data) and get_dimension_values (specific dimension values).

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?

Usage is implied: use this tool to get metadata and structure for a Eurostat dataset before querying data. However, there is no explicit guidance on when to prefer this over siblings like get_dimension_values or describe_table, nor any exclusions or alternative mentions.

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