get_data_quality

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

The readOnlyHint annotation already establishes that this is a safe read operation. The description adds minimal behavioral context by stating the summary is 'computed from raw observations,' which is useful for understanding the data source but does not reveal much else about behavior such as aggregation scope or latency. With annotations covering safety, this is adequate but not rich.

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 a single concise sentence that front-loads the core purpose and then adds one relevant detail about the computation source. Every word earns its place, and there is no redundant or vague 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 there are no parameters, the output schema is present, and the readOnlyHint annotation is provided, the description is complete enough for an agent to select and invoke this tool correctly. It specifies what kind of summary is returned and the key dimensions covered.

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 has zero parameters, so there is no parameter documentation burden on the description. Baseline for 0 parameters is 4, and the description appropriately focuses on what the tool returns rather than parameters.

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 verb 'Get' and the resource 'data quality summary,' and specifies the key dimensions: completeness, coverage, and validity across the cohort. It does not explicitly differentiate from sibling tools like get_diagnostics or get_composite_score, but 'data quality summary' is specific enough to identify the tool's purpose.

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 the tool should be used when a data quality summary over a cohort is needed, and the phrase 'across the cohort' gives some context. However, it provides no explicit guidance on when to use this tool versus alternatives such as get_diagnostics or get_cohort_distribution, and it lacks any exclusions or alternative routing.

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