get_live_pit_stops

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

With no annotations provided, the description carries full burden. It discloses the tool returns 'PitStopsResponse with pit stop durations and statistics' and mentions data availability constraints ('2023+, OpenF1 data availability'), but lacks details on rate limits, authentication needs, error conditions, or pagination behavior for a data-fetching tool.

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 appropriately sized and well-structured with clear sections (purpose, Args, Returns, Example). Every sentence adds value, though the example section could be slightly more concise. The information is front-loaded with the core purpose stated 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 has an output schema (Returns mentions 'PitStopsResponse'), the description doesn't need to detail return values. It covers purpose, parameters, and usage examples adequately for a data retrieval tool. However, without annotations, it could better address behavioral aspects like data freshness or API 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?

Schema description coverage is 0%, so the description must compensate fully. It successfully adds meaning for all 4 parameters: explains 'year' constraints, provides 'country' examples, clarifies 'session_name' options with default, and describes 'driver_number' filtering purpose. The Args section comprehensively documents parameter semantics beyond basic schema titles.

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 pit stop analysis with crew timing from OpenF1.' It specifies the verb ('Get'), resource ('pit stop analysis'), and data source ('OpenF1'), distinguishing it from siblings like get_laps or get_stints_live which focus on different race data aspects.

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 usage through examples showing when to use optional parameters, but it doesn't explicitly state when NOT to use this tool or name alternatives among siblings. The examples illustrate filtering by driver number vs. getting all stops, offering practical guidance.

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