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darshjoshi

Pitwall F1

by darshjoshi

Plot Driver Telemetry Channels

plot_driver_telemetry_comparison
Read-onlyIdempotent

Generate four subplots comparing speed, throttle, brake, and gear telemetry between two F1 drivers for the same lap.

Instructions

Plot comprehensive telemetry comparison (speed, throttle, brake, gear) between two drivers for the same lap.

Args: year: Season year gp: Grand Prix name driver1: First driver identifier (3-letter code) driver2: Second driver identifier (3-letter code) lap_number: Lap number to compare session: Session type (R=Race, Q=Qualifying, FP1/FP2/FP3=Practice, S=Sprint)

Returns: ImageContent with 4 subplots showing speed, throttle, brake, and gear data for both drivers

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
gpYes
yearYes
driver1Yes
driver2Yes
sessionNoR
lap_numberYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataYes
typeYes
_metaNo
mimeTypeYes
annotationsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

B3.3/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and destructiveHint=false, so the safety profile is covered. The description adds return format (4 subplots showing speed, throttle, brake, gear), which is useful context, but since an output schema exists, this is somewhat redundant and it does not cover rate limits or auth needs.

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 the core purpose, then uses an 'Args:' block and a 'Returns:' block. Given 0% schema coverage, listing parameter meanings earns its place. No wasted sentences.

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

Completeness3/5

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

It covers purpose, parameters, and return shape, and annotations cover safety. However, it omits guidance on when to choose this tool over sibling plot_telemetry_comparison and plot_multi_telemetry_comparison, which is a notable gap for correct tool selection.

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?

Schema description coverage is 0%, so the description must carry the burden. It lists all six parameters with meaning and format hints: 'year: Season year', 'gp: Grand Prix name', 'driver1/2: 3-letter code', 'lap_number: Lap number to compare', and 'session' with explicit values (R, Q, FP1/FP2/FP3, S). Some details (e.g., exact GP naming, year range) remain ambiguous, but it compensates well.

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 states a specific verb (plot), resource (telemetry comparison), and scope (between two drivers for the same lap), and enumerates the channels (speed, throttle, brake, gear). It is clear but does not distinguish itself from sibling plot tools such as plot_telemetry_comparison or plot_multi_telemetry_comparison.

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?

The description gives no explicit guidance on when to use this tool versus alternatives. The only implicit context is 'for the same lap', which is insufficient to route among the many sibling comparison/plot tools.

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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