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. It discloses what the call yields (filter keys, valid values, source URL) and its role as a prerequisite step, but says nothing about permissions, rate limits, or failure behavior for an unknown dataset_id — the kind of behavioral detail an unannotated tool would ideally supply.

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?

Front-loaded with the purpose, then the usage rule, then returns — a sensible ordering with no filler sentences. Slight redundancy: the 'Returns: DatasetDetail with ...' block restates fields that an output schema already publishes, so a sentence is spent duplicating structured data.

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 one-parameter read tool with a rich output schema, the description supplies everything needed: what it does, why to call it first, and concrete example filter keys/values. It is essentially complete, with only minor gaps around error cases and freshness of the discovered metadata.

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 100%: the single `dataset_id` parameter already documents its curated-ID nature, case-insensitivity, and discovery path with examples. The description's concrete filter keys and values are about the *result*, not about how to set the parameter, so it adds little beyond the schema — the baseline 3 applies.

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 names a specific verb and resource ('Describe a dataset's filterable dimensions, returnable measures, and source') and explicitly separates itself from the sibling `get_data`, which it positions as the downstream step. An agent can distinguish this metadata-inspection tool from retrieval tools without opening a schema.

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?

It gives a clear precondition ('Use this before calling get_data on a new dataset') and names the alternative being deferred to. It does not explain when this is unnecessary (e.g., a dataset already described) or route to the other siblings `search_datasets` / `list_curated` for discovery, so it stops just short of a full when/when-not statement.

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