summarize_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 clearly discloses that it returns aggregates, never dumps rows into the conversation, and provides the exact return structure. It does not explicitly state whether the dataset is modified, but the summarize intent and output shape strongly imply a read-only operation.

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 and front-loaded: purpose, when to use, arguments, return format, and an example. Every section earns its place without unnecessary filler.

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 low complexity (one parameter) and the detailed return schema embedded in the description, an agent has everything needed to invoke the tool correctly. The example call is clear and the return shape is fully specified.

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 single parameter has no schema description (0% coverage), so the description must compensate. It does by specifying 'Exact dataset name' and giving an example, which is adequate for a single string parameter. It could add guidance on discovering valid dataset names, but the example removes most ambiguity.

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 begins with a specific verb and resource: 'Summarize a dataset with aggregates instead of raw rows.' It further distinguishes itself from row-returning siblings by explaining it returns row count, numeric stats, and enum breakdowns rather than rows.

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 explicit context: 'Use this when the user asks for totals or breakdowns' with concrete examples. It does not explicitly name alternatives such as search_rows, but the contrast with raw rows implicitly routes the agent away from row-level tools.

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