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

No annotations are provided, so the description carries the full disclosure burden. It openly states the dataset is not exhaustive (recall ~0.6), that missing detections are not proof of absence, and that key fields are model estimates rather than surveyed values. These caveats are exactly the behavioral context an agent needs before interpreting results.

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 front-loaded with purpose, then names the alternative, then gives data-quality caveats in logical order. Every sentence adds necessary context for interpretation or safe use, with no filler or repetition.

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?

The tool has no output schema, so the description must supply return semantics and caveats; it does, covering kWp, system counts, recall, and estimate status. It also gives a clear next step (get_data_quality_reference), making the tool safely callable even without reading sibling schemas.

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 description coverage is 100% because both dpt and top_n have inline descriptions. The tool description reinforces that department selection is the only real filter, but adds little beyond what the schema already documents for the parameters. 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 first sentence uses a specific verb ('Get') with a clear resource ('installed rooftop-PV capacity (total kWp) and system counts') and scope ('one or all French départements'). It explicitly differentiates itself from aggregate_detection_capacity, so an agent can distinguish it from the closest sibling.

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 explicitly names aggregate_detection_capacity as the alternative when a filtered subset is needed, and instructs the agent to call get_data_quality_reference before assessing fitness for a specific use case. This provides clear when-to-use and when-not-to-use guidance.

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