describe_dataset

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

With no annotations provided, the description takes full responsibility for behavioral transparency. It clearly describes what the tool returns (DatasetDetail with specific fields) and its read-only nature is implied (no destructive actions mentioned). It lacks explicit mention of side effects or authorization, but for a metadata lookup tool, this is sufficient.

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 concise and well-structured: it leads with the primary purpose, then contextualizes usage, and finally details return fields. Every sentence adds value without repetition or fluff.

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 simplicity (one parameter) and the presence of an output schema, the description provides complete context. It covers what the tool does, when to use it, what the response includes, and even special behavior for insurance datasets. An agent should be able to use the tool correctly from this description.

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 description coverage is 100% with a well-described 'dataset_id' parameter. The description adds minimal parameter-specific insight beyond the schema, only emphasizing that the ID can be discovered via search or list functions. Baseline of 3 is appropriate as the schema already carries the semantic load.

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 it describes a dataset's metadata (dimensions, measures, source, etc.), using specific verbs like 'describe' and naming the resource. It distinguishes from siblings like 'get_data' (retrieves actual data) and 'list_curated' (lists datasets) by explicitly saying 'Use this before calling get_data on a new dataset.'

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 explicit when-to-use guidance: 'Use this before calling get_data on a new dataset.' It also tells what you get from it (valid filter keys, values, measure aliases, etc.), which implies when it is appropriate to call.

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