get_session_results

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

With no annotations provided, the description carries the full burden. It effectively discloses the tool's scope (2018-present), data authority ('authoritative data'), and return format ('complete finishing order, driver info, teams, times, points, grid positions'). However, it lacks details on error handling, rate limits, or authentication needs, which would be beneficial 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 bold headings, bullet points, and examples, making it easy to scan. Every sentence adds value—from the primary purpose to usage rules, parameters, returns, and examples—with no redundant information.

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 tool with 3 parameters, no annotations, and an output schema, the description is complete. It covers purpose, usage guidelines, parameter details, return values, and examples, providing all necessary context for an agent to invoke it correctly without relying on external documentation.

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?

Given 0% schema description coverage, the description compensates fully by explaining each parameter: 'year' as season year with range (2018-2025), 'gp' as Grand Prix name or round number with examples, and 'session' with codes and meanings (e.g., 'R' for Race, 'Q' for Qualifying). It adds essential meaning beyond the bare schema.

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 explicitly states the tool retrieves 'Formula 1 session results (2018-present)' and lists specific use cases like race winners, qualifying results, and practice classifications. It clearly distinguishes this as the primary tool for results data versus other siblings like get_analysis or get_standings, which serve different purposes.

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: 'ALWAYS use this tool instead of web search' for F1 results questions and lists specific scenarios (e.g., race winners, qualifying results). It also includes a 'DO NOT use web search' directive, offering clear alternatives and exclusions, though it doesn't differentiate among sibling tools directly.

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