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. It discloses the data source and coverage, which are useful behavioral traits, but lacks details on error handling, rate limits, or authentication needs. The description does not contradict any annotations, as none are given, and it adds some 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?

The description is well-structured and front-loaded with the core purpose, followed by sections for data source, parameters, returns, example, and notes. Each sentence adds value without redundancy, making it efficient and easy to parse for an AI agent.

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 an output schema (implied by the returns section), the description is largely complete. It covers purpose, parameters, return format, and data nuances, but could improve by addressing potential errors or usage constraints relative to siblings.

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?

Schema description coverage is 0%, so the description must compensate. It effectively explains all three parameters (year, event, session) with examples and constraints (e.g., year 2018+, session types like R, Q), adding meaningful semantics beyond the bare schema. However, it does not fully detail event formats (string vs. integer) beyond the example.

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 speed trap and top-speed data') and resources ('for all drivers in a session'), distinguishing it from siblings like get_fastest_laps or get_sector_times by focusing on speed trap metrics. It also specifies the data source (FastF1 Live Timing) and coverage period (2018-present), adding further clarity.

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?

Usage is implied through the description of data coverage and parameters, but there is no explicit guidance on when to use this tool versus alternatives like get_fastest_laps or get_sector_times. The example provides context for parameter usage, but no when-not or alternative tool recommendations are included.

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