light_pollution_map

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

No annotations are provided, so the description carries the full burden. It mentions the return format ('grid of light pollution data points including brightness, Bortle class, and SQM') and a default zoom level, but lacks critical behavioral details: it doesn't specify data sources, accuracy, rate limits, error handling, or whether this is a read-only operation. For a tool with no annotations, this is a significant gap.

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 well-structured and front-loaded: the first sentence states the purpose, followed by return details and parameter explanations. Every sentence adds value without redundancy. It's appropriately sized for the tool's complexity.

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 no annotations, 0% schema coverage, and an output schema (which handles return values), the description is moderately complete. It covers purpose, return content, and parameter meanings, but lacks behavioral context like data freshness or limitations. For a data-fetching tool, this is adequate but has clear gaps.

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 0%, so the description must compensate. It adds meaningful semantics: it explains that parameters define a 'Bounding box coordinates' and that 'zoom' controls 'grid resolution' with a default and effect ('Higher zoom means more detailed grid'). This clarifies all 5 parameters beyond their schema types, though it could detail coordinate formats or zoom range.

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 tool's purpose: 'Get light pollution data for a specific area.' It specifies the verb ('Get') and resource ('light pollution data'), and distinguishes it from siblings like weather or celestial tools by focusing on pollution metrics. However, it doesn't explicitly differentiate from potential overlapping tools (e.g., if 'analysis_area' also handles pollution), keeping it at 4 instead of 5.

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 provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'analysis_area' or 'get_nightly_forecast', which might offer related data, nor does it specify prerequisites or exclusions (e.g., coordinate validity). Usage is implied by the purpose but lacks explicit context.

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