get_dataset_health

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

Annotations already declare readOnlyHint=true, and the description adds meaningful behavioral context: it explains what the grade represents, that it combines multiple components, and that it deliberately avoids judging whether actual values are correct. This helps agents set expectations. It could still name more details about return shape, but the existing text is effective.

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?

Three dense sentences, each adding distinct information, with the core purpose front-loaded. The phrase 'for a dataset (B4)' is slightly cryptic and could confuse agents, but it does not materially hurt the definition.

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 simple one-parameter tool with no output schema, the description covers the purpose, the combined inputs, and a clear boundary of what it does not measure. It would benefit from naming the exact structure/format of the breakdown, but it is broadly complete.

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 schema fully describes the single 'dataset' parameter as a string identifier, so the description does not need to add parameter-level syntax. The description adds no specific detail about the identifier format, but that is the low bar because schema coverage is 100%.

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 identifies the tool as producing a composite quality grade (A–F) for a dataset, listing the specific factors that go into it. It also adds the caveat that it grades structure and completeness, not value correctness. However, it does not explicitly differentiate itself from related sibling tools like get_schema_drift or data_health_radar.

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 implies this should be used when an overall dataset quality assessment is needed, especially for structural and completeness issues. It does not explicitly say when not to use it or which sibling tool might be a better alternative, so the agent must infer the use case.

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