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

The description goes far beyond the readOnly/idempotent annotations, disclosing the 30-minute update interval, the 2-hour verdict freshness window, the rule that stale rows 'never speak for the group', indeterminate-read exclusion semantics, the exact meaning of 'verdict: null', and the empty-cameras-list edge case. It never contradicts the annotations, which all align with a read-only, idempotent operation.

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?

Three dense paragraphs with the purpose front-loaded and every sentence earning its place: field names, value ranges, null semantics, staleness rules, and edge cases. Because there is no output schema, the length is justified — it substitutes for structured return documentation rather than padding.

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 tool with no output schema and nested return data, the description thoroughly documents the response shape (vision fields, history, hourly averages, verdict), critical null semantics, freshness behavior, and edge cases. The only notable gap is the undocumented 'limit' parameter, plus 'trip-planning stats' is named but not elaborated beyond the hourly averages.

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?

The single parameter 'limit' has 0% schema description coverage and the description never mentions it — no explanation of what it caps (cameras returned? history points?), its relationship to the row structure, or how it interacts with the verdict. With coverage this low, the description was required to compensate, and it does not.

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?

States a specific resource ('Landmark camera visibility'), the data source ('vision-model readings of public webcams... Golden Gate Bridge Caltrans set'), and the delivered artifacts (per-camera history and trip-planning stats). The opening sentence clearly differentiates this from generic weather or condition lookups by anchoring it to webcam vision readings.

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 'honesty labels' section gives strong contextual guidance about what the data is and is not — 'not an NWS station, not a forecast' — which tells an agent when this tool's output can and cannot be trusted. It stops short of naming sibling alternatives or explicit when-to-use/when-not-to-use conditions, so it earns a 4 rather than a 5.

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