describe_dataset

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool returns schema and metadata, which implies a read-only operation, but doesn't clarify aspects like whether it requires authentication, rate limits, error handling, or what the output format looks like (though an output schema exists). The mention of shortcut ids adds some context, but overall, the description lacks sufficient behavioral details for a mutation-free tool with no annotation coverage.

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 highly concise and front-loaded: the first sentence clearly states the purpose, and the second sentence provides essential usage guidance with shortcut examples. There's no wasted text, and every sentence earns its place by adding specific value, making it easy for an agent to parse quickly.

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 the tool's low complexity (1 parameter, no nested objects) and the presence of an output schema (which handles return values), the description is reasonably complete. It covers the purpose, provides key parameter semantics with shortcut ids, and offers basic usage hints. However, it lacks behavioral details like authentication or error handling, which are somewhat important even for a read-only tool with no annotations, keeping it from a perfect score.

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 has 1 parameter with 0% description coverage, so the description must compensate. It adds meaning by specifying that 'dataset_id' should use 'shortcut ids' like "fgj", "911", "ids", "aire", which clarifies the expected format and valid values beyond the schema's generic string type. This is valuable semantic information, though it doesn't cover all potential dataset IDs or explain the parameter's role in detail.

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 states the tool's purpose: 'Return schema and metadata for a dataset on datos.cdmx.gob.mx.' This specifies the verb ('Return'), resource ('schema and metadata'), and target ('dataset on datos.cdmx.gob.mx'). However, it doesn't explicitly differentiate from siblings like 'list_datasets' (which likely lists available datasets) or 'query_records' (which queries data), though the distinction is somewhat implied by the focus on schema/metadata versus data.

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 provides some usage context by mentioning 'Use shortcut ids: "fgj", "911", "ids", "aire".' This implies when to use the tool (for these specific datasets) but doesn't explicitly state when to use it versus alternatives like 'list_datasets' or 'query_records'. There's no guidance on prerequisites, exclusions, or detailed comparisons, leaving the agent to infer usage from the purpose alone.

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