get_weather_forecast

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

With no annotations, the description carries the burden of behavioral disclosure. It reveals that the API choice depends on a token, which is a key behavioral trait. However, it does not cover error handling, rate limits, or data freshness, leaving gaps in transparency.

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 concise with only two sentences. The first sentence defines the purpose, and the second adds essential implementation detail. No redundant or extraneous information is present.

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 existence of an output schema (not shown) and the tool's relative simplicity, the description covers the key aspects: what it does, where, and the backend logic. It omits minor details like default days, but those are in the schema. A score of 4 reflects near-completeness.

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 coverage is 100%, so the description adds minimal value beyond what the schema already provides. The mention of 'PAGASA TenDay API' hints at the day range but does not provide additional semantic context beyond the schema's existing descriptions.

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 weather forecast for a Philippine location.' It uses a specific verb ('Get') and resource ('weather forecast') with a geographic scope, effectively distinguishing it from sibling tools like get_air_quality or get_active_typhoons.

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 implies usage context by explaining the backend selection (PAGASA vs Open-Meteo based on token), but it does not provide explicit guidance on when to use this tool over alternatives like get_weather_alerts or get_solar_and_climate. Usage guidance is implied but not fully elaborated.

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