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

No annotations are provided, so the description carries the full behavioral burden. It does this well by candidly warning that this is a detection dataset with estimated recall ~0.6, that missing detections are not proof of absence, and that kwp, surface, tilt, and azimuth are model estimates. It does not cover response truncation, ordering, or error behavior, but the disclosed data-quality caveats are substantial and high-value.

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 compact and every sentence adds value: the core purpose, the intended use, the critical recall caveat, the model-estimate warning, and the pointer to get_data_quality_reference. It is front-loaded with the action and scope and keeps secondary warnings in natural supporting positions.

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?

There is no output schema, yet the description names the key return aspects: footprint geometry and the attributes kwp, surface, tilt, and azimuth. It also gives a necessary caution about interpreting those values. It does not describe the response envelope or coordinate validation, but for a bbox query tool with a clear purpose and data-quality caveats, the essential context is present.

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 only 20%; only max_count is explained in the schema. The description compensates partially by specifying WGS84 lon/lat and indicating a small-area query, but it does not define the bounding-box corner semantics, coordinate order, or edge cases such as antimeridian crossing. For a tool with five required coordinate parameters, more parameter-level detail would be needed to fully bridge the schema gap.

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 opens with a specific verb and resource: 'Get rooftop-PV detections within a geographic bounding box (WGS84 lon/lat), including footprint geometry.' This makes the spatial scope and return content clear. It also gives an intended context ('map-style spatial queries over a small area') that distinguishes it from broader sibling tools like search_detections.

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 states when the tool is appropriate: 'Intended for map-style spatial queries over a small area.' It also explicitly directs the agent to a sibling, get_data_quality_reference, before assessing fitness for a use case. It does not fully spell out when not to use this tool versus search_detections, so it falls just short of exhaustive routing guidance.

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