get_schedule

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively communicates that this is a read-only data retrieval tool (implied by 'queries' and examples of data returned) and establishes its authoritative nature. However, it doesn't mention potential limitations like data freshness, rate limits, or error conditions that would be helpful for a tool with no annotations.

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 clear sections (purpose, usage guidelines, parameters, returns, examples) and uses bold formatting effectively. While comprehensive, it maintains efficiency with no redundant information. Every sentence serves a clear purpose in guiding tool selection and usage.

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 complexity (5 parameters with 0% schema coverage, no annotations) and the presence of an output schema, the description provides excellent context. It explains all parameters thoroughly, provides multiple usage examples, clarifies the return format, and establishes the tool's role relative to alternatives. The output schema existence means the description doesn't need to detail return values.

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 explanations for all 5 parameters. Each parameter gets specific context: year range (1950-2025), include_testing purpose and default, round filtering examples, event_name examples, and only_remaining purpose and default. The examples further illustrate parameter usage.

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 this is the 'PRIMARY TOOL for ALL Formula 1 calendar and schedule queries' and provides specific examples of what it handles (race dates, session schedules, track information). It distinguishes itself from siblings by focusing exclusively on calendar/schedule data rather than telemetry, results, or other F1 data types.

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 explicit guidance on when to use this tool ('ALWAYS use this tool instead of web search for any F1 calendar questions') and when not to use alternatives ('DO NOT use web search for F1 schedules'). It establishes this as the authoritative source for schedule data among the sibling tools.

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