fetch_weather

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

With no annotations, the description must carry the behavioral burden. It conveys read-only external fetching via 'live' and 'using Open-Meteo', which is helpful, but it does not disclose error behavior for unknown cities, units, network/API dependencies, or the shape of the returned forecast. These gaps are notable because no annotations or output schema fill them.

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?

A single front-loaded sentence with no filler and no repetition of schema details. Every word adds meaning: operation, scope, live-like behavior, and data source are all 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?

For a low-complexity tool, the description is adequate for selection and initial invocation, but without an output schema it should say more about what the forecast contains, such as temperature or conditions, and how failures are handled. The absence of annotations increases the burden, leaving meaningful 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?

The input schema already provides full coverage for the only parameter, city, including examples. The description adds only 'any city', which reinforces the schema's unconstrained string type. No additional parameter semantics are supplied, so the baseline 3 applies.

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 uses a specific verb ('Fetches') and object ('live weather forecast') and scopes it to 'any city', naming the data source. This makes the tool's function unambiguous and distinguishes it from the sibling tools, none of which involve weather or external data retrieval.

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?

It clearly implies the intended use: whenever a live weather forecast for a city is needed. It does not explicitly mention exclusions or alternatives, but the sibling tools are unrelated, so no alternative-routing is necessary. It only lacks an explicit 'use this instead of X' style statement to earn a 5.

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