fastf1-mcp-server

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

With no annotations provided, the description carries full burden and does well by specifying data source, coverage range, and important behavioral notes about accurate laps and stint numbering. It doesn't mention performance characteristics, rate limits, or authentication requirements, but provides substantial operational context beyond basic functionality.

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?

Excellent structure with clear sections: purpose statement, data source info, parameter explanations, return format, concrete example, and important notes. Every sentence earns its place, and information is front-loaded with the core functionality stated first. No wasted words while maintaining completeness.

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, no annotations, but has output schema), the description is remarkably complete. It covers purpose, parameters, return format with specific fields, provides a concrete example, and includes important behavioral notes about data accuracy and stint numbering. The output schema existence means return values don't need explanation in the description.

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 compensates well by explaining all three parameters in the Args section with clear semantics: year range (2018+), event format (name or round number), and driver as optional filter. The example further clarifies usage. It doesn't provide format details for driver codes or event names, but adds substantial value 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 clearly states the tool 'Analyze tire stints for a race' with specific data source (FastF1 Live Timing) and coverage (2018-present). It distinguishes from siblings by focusing on tire stint analysis rather than results, standings, telemetry, or other race data. However, it doesn't explicitly contrast with similar tools like get_race_pace or get_lap_times.

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 through the data source and coverage information, and the example shows a specific use case. However, it doesn't provide explicit guidance on when to use this tool versus alternatives like get_race_pace or get_lap_times, nor does it mention any prerequisites or exclusions.

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