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 transparently discloses the data source (Open-Meteo via 787daily's cached endpoint), the exact return content (hourly temperature, wind, conditions, daily high/low, rain chance, UV, sunrise/sunset), and the required slug format. It does not discuss error behavior, freshness, or rate limits, but for a read-only weather lookup the main behavioral traits are well covered.

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 information-dense but well organized: it front-loads the core deliverable (current plus hourly plus daily), then enumerates the key metrics, and closes with the data source. Every clause adds useful context, and the length is justified by the amount of return detail described.

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 the single parameter, the fully documented schema, and the absence of an output schema, the description is complete enough for an agent to understand what the tool returns and how to invoke it. It covers the input domain, the output structure, the units, and the data source, leaving no critical gap for correct selection and use.

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%, so the baseline is 3. The description adds value by explicitly stating that the parameter covers all 78 municipalities and by reinforcing the slug format with real examples, helping the agent understand valid inputs beyond the schema text.

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 and resource ('get' weather for any of Puerto Rico's 78 municipalities) and clearly differentiates the tool's scope: current conditions plus a 24-hour hourly forecast and a 7-day daily outlook. It also gives concrete slug examples, making the tool easy to distinguish from siblings like get_conditions or get_by_municipality.

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 clear context for when to use the tool: any time weather data for a Puerto Rican municipality is needed, keyed by slug. It does not explicitly name alternatives or exclusion criteria, but the detailed scope and input format make the usage context obvious.

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