fetch_current_weather

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 details the return dictionary keys (city, country, temperature, feels-like, min/max, humidity, weather description) and states that an 'error' key is returned on failure. This provides solid insight into the tool's behavior, though it does not cover edge cases like unknown cities or units, which would push it to a 5.

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 and well-structured: a single purpose sentence followed by Args and Returns sections that clearly delineate parameter and return behavior. Every sentence contributes value without redundancy, and the format follows expected conventions for Python docstrings.

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 simple tool with one parameter and no annotations, the description covers the purpose, the parameter, the return structure, and error handling. Although an output schema is present, the description supplements it by naming the specific fields and the error key, making the tool fully understandable. It lacks only minor context (like units or city validation), which is not critical given the simplicity.

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 has one parameter 'city' with no description (0% schema coverage). The description's Args section says 'city (str): Name of the city to fetch weather data for,' which adds a minimal clarification that it is a name string. This is helpful but does not elaborate on valid formats (e.g., 'London' vs 'London, UK'). The parameter is simple, so the description provides adequate but not rich semantics.

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 begins with 'Fetch the current weather conditions for a given city,' which uses a specific verb 'Fetch' and clearly identifies the resource (current weather conditions) and the target (a city). This distinguishes it from its sibling tool 'fetch_weather_forecast,' which is for forecasts, making the purpose unambiguous.

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 for current conditions but does not explicitly state when to use this tool versus the sibling 'fetch_weather_forecast.' The context is clear from the name and wording ('current weather'), but there is no explicit exclusion or mention of an alternative. Since the sibling is named for forecasts, the intended use is strongly implied, so a 4 is appropriate.

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