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

With no annotations, the description carries the full burden, and it exceeds expectations: it states the dataset is detection-based with estimated recall ~0.6, warns that a missing detection does not mean no installation exists, and flags kwp/surface/tilt/azimuth as model estimates. This is substantive data-quality disclosure well beyond the schema.

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 action, use case, and caveats are front-loaded in a compact note; no sentence is wasted. The data-quality explanation earns its place because it prevents misinterpretation of the output.

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 one-optional-parameter tool with no output schema, the description covers what is returned (yearly counts and capacity by département based on first-seen year), how to interpret estimates, and where to get context (get_data_quality_reference). No critical gap remains for selecting and invoking the tool.

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% and the schema already defines dpt as a French département code with 'Omit for all département'. The description adds no parameter-level detail beyond 'by département', so the 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 names a specific verb ('Get'), a concrete resource ('yearly system counts and capacity by département'), and a distinguishing aggregation key ('first-seen imagery year'). It is clear what the tool returns, but it does not explicitly contrast itself with the closely named sibling get_department_capacity_stats, so it falls short of full sibling differentiation.

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 gives a clear use case ('tracking apparent PV deployment growth over time') and explicitly instructs the caller to consult get_data_quality_reference before assessing fitness. It does not spell out when to prefer get_department_capacity_stats or aggregate_detection_capacity instead, so exclusions and alternatives are incomplete.

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