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 key traits: data sources (Ergast API and FastF1), coverage ranges (1950-present for season, 2018-present for event filter), and the effect of the event parameter on data retrieval. However, it lacks details on rate limits, authentication needs, or error handling, which are minor gaps.

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 and appropriately sized, with clear sections (Args, Returns, Example, Note) that make it easy to scan. Every sentence adds value, such as data source details and usage notes, with no redundant or wasted information. It is front-loaded with the core purpose.

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, no annotations, and the presence of an output schema (implied by the Returns section), the description is complete enough. It covers purpose, usage, parameters, return values, examples, and behavioral context like data sources and coverage, leaving no significant gaps for the agent to operate effectively.

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 description coverage is 0%, so the description must compensate. It adds significant meaning beyond the input schema: it explains that 'year' is the season year, 'event' is an optional race name or round number for filtering, and clarifies that providing 'event' returns only drivers who participated in that session. This fully documents both parameters with practical context.

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: 'List all drivers in a season, optionally filtered to a specific event.' It specifies the verb ('list'), resource ('drivers'), scope ('season'), and optional filter ('event'), and distinguishes it from siblings like get_driver_info or get_driver_standings by focusing on listing rather than retrieving detailed info or standings.

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 vs. alternatives: it explains that without an event, it uses Ergast API data (1950-present), and with an event, it uses FastF1 session data (2018-present). This includes data source differences and coverage constraints, helping the agent choose based on year and filtering needs.

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