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PrimeGoose

f1-telemetry-mcp

by PrimeGoose

get_tyre_wear

Retrieve tyre wear percentage per corner per lap plus average wear rate to analyze degradation trends from F1 telemetry.

Instructions

Tyre wear (%) per corner per lap and average wear rate.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.2/5.0
Behavior3/5

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

With no annotations, the description carries the full disclosure burden. It does reveal genuinely useful behavior: the metric is a percentage, it is broken down per corner and per lap, and an averaged wear rate is also returned. However, it says nothing about which session/driver the data is scoped to, whether results are paginated or large, or what happens if no session is active.

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?

A single, front-loaded sentence that states the granularity and unit with no filler. It is appropriately sized, though the terseness leaves the scoping question unaddressed rather than using the space for it.

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

Completeness2/5

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

For a tool with no parameters, no annotations, and no output schema, the description is the only place to explain how the data is scoped — which session, which driver, which lap range. It omits that entirely, so an agent cannot tell whether the call needs implicit session context or returns everything. This is a meaningful gap for a data-retrieval tool.

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 declares zero parameters, so there is nothing for the description to disambiguate and no coverage gap to compensate for. The baseline for a parameterless tool applies.

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

Purpose4/5

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

The description names a specific resource (tyre wear per corner per lap, plus average wear rate) with a unit, which cleanly separates it from get_car_telemetry and get_lap_times. It is framed as a return-value summary rather than an action, so the verb is only implied, but the resource is unambiguous.

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

Usage Guidelines2/5

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

There is no when-to-use guidance, no mention of how this differs from get_car_telemetry (which may also carry tyre data), and no indication of prerequisites or session context. The agent must infer everything about invocation.

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