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DanielTomaro13

sportsdata-mcp

jolpicaf1_results

Read-onlyIdempotent

Fetch Formula 1 race results by season and round, including finishing order, grid, laps, time/status, points, and lap time data as JSON.

Instructions

Race results: finishing order, grid, laps, time/status, points and fastest lap.

Returns: {MRData:{RaceTable:{Races:[{season, round, raceName, Results:[{position, positionText, points, grid, laps, status, Driver:{driverId, code}, Constructor:{constructorId}, Time:{millis, time}, FastestLap:{rank, lap, Time, AverageSpeed}}]}]}}} — all values are STRINGS

Example: 2024 round 1 {"season": "2024", "round": "1", "format": "json"}

Auth: none needed.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoPage size (max 100).
roundYesRound number, or 'last'. Required — part of the URL path.
formatNoLeave as json.json
seasonYesSeason year, or 'current'. Required — part of the URL path.
Behavior4/5

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

Annotations already declare the tool read-only, idempotent, and open-world. The description adds useful behavioral context beyond annotations: 'Auth: none needed' and 'all values are STRINGS'. It does not mention rate limits or pagination, but the annotations cover the safety profile, so this is adequate.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

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

The description is front-loaded with a concise summary, followed by a detailed return structure and a brief example. The return structure is somewhat verbose but necessary to convey the nested output format. Overall, it is efficient and well-organized, though it could be slightly more compact.

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

Completeness4/5

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

The detailed return structure compensates for the absence of an output schema, and the example clarifies the required parameters. However, it does not explain the meaning of specific result fields (e.g., status, positionText) or handle edge cases like invalid season/round values. For a simple read-only tool, this is mostly complete.

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?

The input schema already documents all four parameters with descriptions at 100% coverage. The description only repeats an example with season and round, adding little parameter-level meaning beyond what the schema already provides. The baseline of 3 applies because the schema does the heavy lifting.

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 identifies the tool as returning race results with specific fields (finishing order, grid, laps, time/status, points, and fastest lap). It distinguishes from sibling tools like jolpicaf1_qualifying and jolpicaf1_sprint by focusing on race results rather than qualifying or sprint sessions.

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 does not explicitly compare this tool to alternatives or state when to use it over sibling tools like jolpicaf1_laps or jolpicaf1_qualifying. It provides an example (2024 round 1) and notes that auth is not needed, giving some context, but lacks explicit 'use this when...' guidance.

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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