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 does add useful behavioral context: the tool combines multiple data sources, returns a structured 'summary' dict, and tells agents to present the summary description to users first. However, it does not disclose limitations like data freshness, geographic coverage, or potential fallback behavior if no monitors are nearby.

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-organized with a clear summary sentence, usage guidance, return value note, and a concise argument list. Each sentence adds value and no information is wasted. It is appropriately sized for a tool with two parameters and moderate 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?

The description covers the tool's purpose, when to use it, what it returns, and key parameter semantics. It also positions itself against siblings. However, because there is no output schema, a few specifics about the response structure (e.g., sections returned for each purpose) could strengthen completeness, though the mention of a structured 'summary' dict is adequate.

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 does so thoroughly: 'location' is explained as Postcode, place name, or "lat,lon", and 'purpose' receives a full list of valid values with explanations. This provides meaning well beyond the bare schema properties and defaults.

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 performs a 'Comprehensive air quality assessment for a location in one call' and specifies what is combined (monitor discovery, current readings, DAQI). It also implicitly distinguishes itself from siblings by positioning as the first tool to call for any location-based air quality question.

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 explicit when-to-use guidance: 'Use this as the first tool call for any air quality question about a location.' It also provides an when-not-to-use example by pointing to the dedicated `trend_analysis` tool for long-term trends. This is strong usage differentiation.

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