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mishan

ac-race-engineer

by mishan

get_driver_notes

Retrieve driver complaint tags with spline positions and lap counts to identify which corners the driver is unhappy with, enabling correlation with telemetry data.

Instructions

Complaint tags the driver pressed in-game while driving (understeer, oversteer, braking, traction, note). Each has a spline position (0..1) directly comparable to corner apex_pos values from lap_summary, plus the lap_count when pressed (current lap = lap_count + 1). Correlate these with telemetry to know which corners the driver is unhappy with.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
session_idNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

With no annotations, the description must disclose behavior. It explains the data structure (spline position comparable to apex_pos, lap_count behavior with +1 offset) and hints at the correlation use case. However, it omits details like default sorting, pagination, read-only guarantee, or effect of session_id parameter. It adds some behavioral context but not comprehensively.

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 three sentences long, gets straight to the point about what the tool returns and how the data relates to other tools. It is front-loaded with key information. The only flaw is the omission of parameter explanations, which would improve completeness without adding much length.

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?

The tool has two optional parameters and a complex output schema (context confirms output schema exists). The description explains the output's conceptual meaning (spline position mapping, lap count) very well, which is valuable. However, it completely ignores the input parameters (limit, session_id), leaving the agent without guidance on how to control the query. This is a significant gap in usability.

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

Parameters1/5

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

Schema coverage is 0%, meaning the schema has no descriptions for limit or session_id. The description does not mention these parameters at all, leaving their purpose and effects entirely undocumented. The description fails to compensate for the zero coverage, providing zero added meaning for the parameters.

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 that this tool retrieves 'complaint tags' pressed by drivers, listing types (understeer, oversteer, etc.) and providing spline position and lap count. The verb 'get' and resource 'driver notes' are explicit. It differentiates from siblings like lap_summary and compare_laps by focusing on subjective driver feedback rather than raw telemetry or lap comparisons.

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?

No explicit guidance on when to use this tool versus alternatives. The description implies it's for correlating driver complaints with telemetry corners, but it does not contrast with siblings like send_driver_message (for sending messages) or lap_summary (for objective data). An agent would have to infer usage from context rather than being directed.

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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