get_session_weather

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

With no annotations provided, the description carries full burden. It indicates this is a read operation (no destructive behavior mentioned) and specifies the data format returned (time-series). However, it doesn't disclose important behavioral traits like rate limits, authentication requirements, error conditions, or whether the data is real-time vs historical. The description adds some context but leaves gaps.

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 efficiently structured with a clear purpose statement, parameter explanations, return value description, and an illustrative example - all in 4 brief sentences. Every element adds value without redundancy, and the information is front-loaded with the core purpose first.

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 tool's moderate complexity (3 parameters, time-series data) and the presence of an output schema, the description provides good coverage. It explains what data is returned and includes a helpful example. However, for a tool with no annotations, it could benefit from more behavioral context about data freshness, availability, or limitations.

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?

With 0% schema description coverage, the description fully compensates by providing clear semantics for all 3 parameters: 'year' is explained as 'Season year (2018+)', 'gp' as 'Grand Prix name or round', and 'session' with specific valid values ('FP1', 'FP2', 'FP3', 'Q', 'S', 'R'). The example further clarifies parameter usage with concrete values.

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 with specific verb ('Get') and resource ('time-series weather data'), listing the exact data fields returned (temp, humidity, pressure, wind, rainfall). It distinguishes from sibling tools by focusing specifically on weather data rather than telemetry, results, or other session information.

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 about when to use this tool - for obtaining weather data during specific F1 sessions. However, it doesn't explicitly state when NOT to use it or mention alternatives (like whether other tools might provide weather data in different formats). The example helps illustrate proper usage.

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