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Surya96t

fastf1-mcp-server

get_schedule

Retrieve the Formula 1 race calendar for any season from 1950 onward, including event details like race names, circuits, dates, and locations.

Instructions

Get the F1 race calendar for a season.

Data source: Ergast API (via FastF1) Coverage: 1950-present

Args: year: Season year (1950-present)

Returns: List of events with: round, raceName, circuitName, country, date, time (if available)

Example: get_schedule(2024) → [ {"round": 1, "raceName": "Bahrain Grand Prix", ...}, ... ]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
yearYes

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 the full burden and adds valuable behavioral context: it discloses the data source (Ergast API via FastF1), coverage range (1950-present), and example output structure. However, it doesn't mention potential limitations like rate limits or authentication needs.

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?

The description is well-structured and front-loaded with the core purpose, followed by data source, coverage, args, returns, and an example. Every sentence adds value without redundancy, making it efficient and easy to parse.

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's low complexity (1 parameter), no annotations, and the presence of an output schema, the description is complete enough. It covers purpose, parameters, returns, and provides an example, leaving no critical gaps for the agent to understand and invoke the tool correctly.

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

Parameters5/5

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

The description adds significant meaning beyond the input schema, which has 0% description coverage. It explains the 'year' parameter's purpose (season year), valid range (1950-present), and provides an example call, fully compensating for the schema's lack of documentation.

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 specific action ('Get'), resource ('F1 race calendar'), and scope ('for a season'), distinguishing it from sibling tools like get_race_results_historical or list_events. It explicitly identifies what data is retrieved and the temporal coverage.

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 by specifying the data source and coverage, but does not explicitly state when to use this tool versus alternatives like get_session_results or list_events. It provides context but lacks direct guidance on tool selection among siblings.

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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