fastf1-mcp-server

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 describes the tool's scope ('historical', 'pre-2018'), data source ('Ergast API via FastF1'), coverage ('1950-present'), and return structure. However, it doesn't mention potential limitations like rate limits, authentication needs, or error conditions, which would be helpful for a read operation.

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, data source, coverage, args, returns, note) and every sentence adds value. It's front-loaded with the core purpose and efficiently communicates necessary information without redundancy.

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 (2 required parameters, no annotations, but with output schema), the description provides excellent contextual completeness. It covers purpose, usage guidelines, parameters, returns, and sibling differentiation. The existence of an output schema means the description doesn't need to detail return values extensively, and it appropriately focuses on when to use the tool versus alternatives.

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?

The schema has 0% description coverage, so the description must compensate. It provides clear semantic meaning for both parameters ('year: Season year', 'round_num: Round number'), which is essential since the schema only specifies integer types. The description adequately explains what each parameter represents, though it doesn't provide validation ranges or examples.

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 with specific verbs ('Get historical race results') and resources ('pre-2018 or when session data unavailable'), and explicitly distinguishes it from its sibling 'get_session_results' by specifying the temporal scope and data source. It provides a clear differentiation from alternatives.

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 explicitly states when to use this tool ('pre-2018 or when session data unavailable') and when to prefer an alternative ('For 2018+ races, prefer get_session_results which has more detail'). This provides clear guidance on tool selection based on temporal context and data availability.

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