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

Annotations already establish read-only, non-destructive behavior, lowering the burden on the description. The description adds useful context by explaining the tool compares total and recent review counts against monthly job volume to judge 'fresh review proof.' It does not reveal algorithmic thresholds or result semantics, but the output schema is present.

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 23-word sentence with the core purpose front-loaded, followed by the input basis. There is no filler, no restatement of the title, and no unnecessary detail.

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 read-only benchmark tool with a fully described four-parameter schema and an output schema, the description is sufficient for correct invocation. It even clarifies that monthlyCompletedJobs can mean jobs, visits, or appointments, adding helpful real-world context without bloating the text.

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?

Schema coverage is 100%: every parameter has a name, type, bounds, and a description in the schema. The description only paraphrases these inputs by grouping them under the notion of 'fresh review proof,' which adds conceptual framing but no new parameter-level detail. Baseline 3 is appropriate.

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 uses a specific verb ('Benchmark') with a concrete resource ('review velocity') and defines the exact question it answers ('whether a business is creating enough fresh review proof'). It also lists the three input dimensions, making it clearly distinct from sibling benchmark tools like run_front_door_benchmark or run_response_time_loss_estimator.

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 gives clear context by specifying the exact use case: assessing whether recent review production keeps pace with completed jobs. It does not explicitly name alternatives or state when not to use this benchmark, but the focused scope on review freshness provides enough routing context for an agent.

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