get_tire_strategy

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 discloses that the tool analyzes data (implying read-only behavior) and returns JSON-serializable format, but it doesn't mention potential limitations like data availability constraints (e.g., '2018 onwards'), rate limits, authentication needs, or error conditions. The description adds some context but lacks comprehensive behavioral traits.

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 well-structured with a clear purpose statement, analysis details, essential context, and organized sections for Args, Returns, and Examples. It's appropriately sized and front-loaded, though the 'Essential for understanding...' sentence could be considered slightly redundant given the initial clarity.

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 (4 parameters, no annotations, but with output schema), the description is fairly complete. It covers purpose, parameter semantics, return format, and provides examples. The output schema existence means the description doesn't need to detail return values, but it could benefit from more behavioral context (e.g., data sources, error handling).

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 must compensate. It provides detailed semantics for all 4 parameters: 'year' (season year from 2018 onwards), 'gp' (Grand Prix name or round number), 'session' (session types like 'FP1', 'R'), and 'driver' (optional identifier, 3-letter code or number, returns all drivers if None). This adds significant meaning beyond the bare schema, though it could specify format examples for 'gp' and 'driver' more explicitly.

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 'gets tire strategy and compound usage for a session' and specifies it analyzes 'tire compounds used throughout a session, including compound types, tire life, and stint information.' This provides a specific verb ('get/analyzes') and resource ('tire strategy and compound usage'), though it doesn't explicitly differentiate from sibling tools like 'get_stints_live' or 'get_laps'.

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 stating it's 'essential for understanding race strategy and tire management,' but it doesn't provide explicit guidance on when to use this tool versus alternatives like 'get_stints_live' or 'get_laps.' The examples show specific use cases, but no direct comparisons or exclusions are mentioned.

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