get_data_quality

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

Annotations already declare readOnlyHint and openWorldHint. The description adds valuable context: caching for 30 minutes, no API key required, and a detailed list of returned metrics. This goes beyond the annotations without contradicting them.

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, starting with purpose, then usage guidance, then a bulleted list of included metrics, and finally caching/auth notes. It is slightly long but every sentence adds value.

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 no output schema, the description provides a comprehensive list of what is returned (timestamps, latencies, counts, percentages) and context on caching and public access. The tool is simple and the description fully explains its behavior and output.

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 has zero parameters and the description explicitly confirms 'Takes no parameters', adding certainty. With no parameters, the baseline is 4, and the description fulfills that.

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 returns public data-quality metrics for the whole dataset, using specific verbs like 'returns' and 'use this to check'. It distinguishes itself from sibling tools by explicitly stating it is not for looking up individual companies or transactions.

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 clear when-to-use guidance ('check whether the dataset is current before relying on it') and what not to use it for ('not to look up any single company'). It mentions it's public and needs no API key, but does not explicitly name alternative sibling tools.

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