Rush SR Lap Analyzer

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

Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=true, so the safety profile is covered. The description adds genuine context beyond that: it accepts either an uploaded RaceStudio CSV file or pasted CSV text, and names the expected export format. It stops short of describing processing limits or error behavior, so 4 rather than 5.

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

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

Three sentences, front-loaded with the use case before the negative routing guidance. No filler, no restatement of the title, and every clause carries information an agent needs for selection.

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

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

An output schema exists, so return values need not be described, and annotations cover the safety profile. The input paths are covered at a high level, but with 0% schema coverage on a nested file object, minimal field-level detail is missing for fully confident invocation.

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

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 load. It does partially compensate by contrasting the two input modes ('upload an AiM RaceStudio CSV export or paste its CSV text'), which maps onto the file and csv_text parameters. However, it says nothing about the nested file_id/download_url/mime_type fields or the 20MB csv_text ceiling, leaving semantic gaps.

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

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

States a specific verb and resource ('analyze my AiM data') and enumerates concrete outputs: lap times, best lap, consistency. It explicitly carves itself away from siblings by excluding lap-comparison and slowest-sector analysis, so an agent can distinguish it from compare_laps and find_time_loss without opening a schema.

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

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

Gives an explicit trigger ('Use this when you want to analyze...') and explicit exclusions ('Do not use for comparing two specific laps or finding one lap's slowest sectors'). The exclusions map directly onto the sibling tools, giving real routing guidance rather than implied usage.

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

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

Annotations cover safety (readOnly, non-destructive) and open-world input. The description adds value by specifying input formats (AiM RaceStudio CSV export or CSV text), which goes beyond the annotations. However, it doesn't discuss output format details or limitations beyond the session-overview exclusion.

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

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

Two sentences, front-loaded with usage and inputs, and includes a clear exclusion. Slightly dense but every clause adds information; no wasted words.

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

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

Given an output schema exists, the description needn't explain return values. It covers usage, inputs, and exclusions. However, it doesn't address required vs optional parameters (lap_a and lap_b required, file/csv_text optional) which would help an agent invoke correctly.

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

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

Schema description coverage is 0%, so the description must compensate. It mentions 'two lap numbers' (lap_a, lap_b) and input via file or CSV text, but doesn't clarify that lap_b is optional or the file object structure. The schema provides property names but no descriptions, so the description partially compensates but leaves gaps.

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

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

States a specific verb+resource (compare two laps) and enumerates the exact outputs: braking points, corner minimum speeds, time delta. This distinguishes it from siblings like analyze_session, compare_sessions, and find_time_loss.

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

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

Explicitly says when to use ('compare my laps or why am I slow in turn 3') and when not to ('Do not use for a full session overview'), directly routing users to analyze_session for that alternative.

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

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=true, so the safety profile is covered. The description usefully adds the accepted input modalities (AiM RaceStudio CSV file uploads or pasted CSV text), which annotations cannot convey, though it does not mention size limits or error behavior for malformed CSVs.

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

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

Two tightly packed sentences with no filler: outcome scope first, input modalities second, exclusions last. Every clause carries decision-relevant information, and the excluded use cases are front-loaded enough to be read before invoking.

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

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

An output schema exists, so return values need not be explained, yet the description still previews the comparison dimensions. The only real gap is the unexplained lap_a/lap_b parameters, which is minor but noticeable given the nested-object and multi-modal input surface.

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

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

Schema description coverage is 0% across 6 parameters including two nested file objects and two 20MB CSV strings. The description partially compensates by explaining the two input modes (file_a/file_b vs csv_text_a/csv_text_b), but never clarifies what lap_a and lap_b select or how they interact with the session-level inputs.

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

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

Specific verb+resource (compare two sessions/drivers/cars) plus an enumeration of exactly what gets compared: best valid lap, delta, sector gains/losses, braking points, corner minimum speeds, top speed. It also explicitly separates itself from compare_laps and analyze_session by saying what it is not for.

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

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

States when to use it (two drivers, two cars, or two sessions on the same track) and gives explicit exclusions: 'Do not use for comparing two laps from one session or for a single-session overview,' which routes the agent to compare_laps and analyze_session respectively.

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

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

Annotations already establish readOnly/destructive=false, so the safety profile is covered. The description adds genuinely useful behavioral context the annotations do not: the accepted input source and format ('AiM RaceStudio CSV export or paste its CSV text'). Auth, rate limits, and result shape are not addressed, but the input constraint is the material one here.

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

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

Three sentences, front-loaded with the usage trigger before mechanism and exclusions, with no filler. Minor awkwardness in 'when asking where am I losing time' but nothing that wastes space.

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

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

An output schema exists, so return values need not be explained. However, with three parameters at 0% schema coverage, none marked required, and an undocumented nested file object, the definition leaves the agent guessing about the lap parameter and which input path is expected.

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

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

Schema description coverage is 0%, so the description must compensate, and it only partially does: it clarifies that file upload and pasted CSV text are alternative input paths, but says nothing about the lap parameter or the nested file object's fields (file_id, download_url, mime_type). Two of three parameters remain semantically unexplained.

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

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

The description states a concrete verb and resource: rank the five distance sectors where one lap loses most against the best valid lap. That is specific enough to distinguish it from compare_laps and compare_sessions without opening either schema.

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

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

It gives a clear triggering question ('where am I losing time') and an explicit exclusion ('do not use for corner speeds or braking point comparison'). It stops short of naming the sibling tool that handles those excluded cases, so the routing is implied rather than fully closed.

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