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DanielTomaro13

sportsdata-mcp

jolpicaf1_laps

Read-onlyIdempotent

Get lap-by-lap times for all F1 drivers in a race, with position and time per lap. Specify season and round to access detailed race timing data.

Instructions

Lap-by-lap times for every driver in a race. LARGE — page it or narrow to one lap.

Returns: {MRData:{total, RaceTable:{Races:[{Laps:[{number, Timings:[{driverId, position, time}]}]}]}}} — one entry per lap, each holding every driver's timing

Example: First laps of 2024 round 1 {"season": "2024", "round": "1", "format": "json", "limit": 100}

Auth: none needed.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoPage size (max 100). A full race is thousands of rows — check MRData.total.
roundYesRound number. Required — part of the URL path.
formatNoLeave as json.json
offsetNoPage offset.
seasonYesSeason year. Required — part of the URL path.
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate a read-only, idempotent operation. The description adds valuable behavioral context: the large payload requiring pagination, the exact return shape, and confirmation that no auth is needed. This exceeds what annotations alone provide.

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 compact and front-loaded: purpose, size warning, return structure, example, and auth in a few sentences. Every sentence earns its place with no redundancy.

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?

No output schema exists, so the description carries the burden of explaining the return structure, which it does explicitly with a nested type sketch. It also covers pagination, example usage, and auth, making it complete for a data-retrieval tool.

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

Parameters3/5

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

Schema description coverage is 100%, so the schema fully documents parameters. The description adds an example call but does not go beyond the schema in explaining parameter semantics. Baseline 3 is appropriate.

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 tool returns 'Lap-by-lap times for every driver in a race' – a specific verb and resource. This distinguishes it from siblings like results, qualifying, and pitstops within the same F1 family.

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

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description warns 'LARGE — page it or narrow to one lap', providing clear context on when and how to use the tool. It also includes a concrete example with season/round and limit. It does not explicitly state when NOT to use it versus alternatives, but the purpose distinction is evident.

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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