describe_dataset

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

No annotations are provided, but the description covers the operation's query nature and return fields. It omits an explicit statement that it is a read-only, non-destructive operation, but the context strongly implies it. A more explicit declaration would raise this to a 5.

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 well-structured with bullet points and front-loaded purpose. However, it repeats the list of dimensions/measures in the Returns section, which could be slightly more concise. Still, it is clear and not overly verbose.

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 moderate complexity, full schema coverage, and presence of output schema, the description covers all needed aspects: purpose, when to use, parameter guidance, and return structure. No gaps remain.

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 already provides a description and examples for the single parameter 'dataset_id'. The description adds further context by explaining how to obtain the dataset_id via search_datasets() or list_curated(), going beyond the schema.

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 describes a dataset's filterable dimensions, returnable measures, units, and source. It uses a specific verb 'Describe' and resource 'dataset', and distinguishes it from siblings like get_data by positioning it as a prerequisite.

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?

Explicitly says 'Use this before calling get_data on a new dataset', providing clear context and directing to an alternative tool (get_data). It also explains what information the tool provides to help the agent decide.

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