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Surya96t

fastf1-mcp-server

get_pit_stops

Retrieve detailed pit stop data from Formula 1 races, including driver, lap number, stop duration, and tyre changes for analysis.

Instructions

Get all pit stops from a race.

Data source: FastF1 Live Timing Coverage: 2018-present

Args: year: Season year (2018+) event: Race name or round number

Returns: Pit stops sorted by lap: driver, lap, stopNumber, duration, tyreFrom, tyreTo

Example: get_pit_stops(2024, "Monaco") → [ {"driver": "LEC", "lap": 28, "stopNumber": 1, "duration": 23.4, "tyreFrom": "MEDIUM", "tyreTo": "HARD"}, ... ]

Note: Duration is calculated from PitInTime (end of in-lap) to PitOutTime (start of out-lap), in seconds.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
yearYes
eventYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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 disclosing data source, temporal coverage, calculation methodology for duration, and return format. It doesn't mention rate limits, caching behavior, or error conditions, but provides substantial behavioral 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured with clear sections (Args, Returns, Example, Note), front-loaded purpose statement, and every sentence adds value. No redundant information - the example and note provide essential clarification without repetition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has an output schema and the description provides comprehensive information about parameters, return format, data source, coverage, and calculation methodology, this is complete for a read-only data retrieval tool. The example concretely demonstrates usage and output structure.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has 0% description coverage, but the description compensates by explaining both parameters: 'year' as 'Season year (2018+)' and 'event' as 'Race name or round number'. It adds value beyond the bare schema, though it could provide more detail about valid event formats or examples.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Get' and resource 'pit stops from a race', specifying the data source (FastF1 Live Timing) and coverage (2018-present). It distinguishes from siblings like get_fastest_laps or get_race_results_historical by focusing specifically on pit stop data.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

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, but doesn't explicitly state when to use this tool versus alternatives like get_stint_analysis or get_race_pace. No explicit exclusions or prerequisites are mentioned.

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

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