The Wheel Atlas

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It explains the return shape (up to 20 wheels), the stored values (Yes/No), and crucially disambiguates blank values as 'not yet checked' rather than 'not approved'. It does not mention rate limits or auth, but for a simple read-style lookup the key behaviors are covered.

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 with no wasted words. The core purpose and matching behavior are front-loaded, followed by the input requirement and the essential return-value nuance. Every clause earns its place.

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?

The tool has only two simple optional-looking string parameters, no output schema, and no annotations. The description sufficiently covers what the agent needs: required inputs, match behavior, result cap, returned values, and the interpretation of blank results. Nothing critical is missing for correct 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 coverage is 100%, so the schema already documents both parameters. The description adds meaning beyond the schema by clarifying that the parameters can be used alone or together, that at least one is required, and that matching is partial. This is genuinely useful semantic context rather than repetition.

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 ('Check') and a specific resource ('UCI approval status for wheels'), and distinguishes itself from siblings such as compare_wheels, get_wheel_price, and search_wheels by focusing on UCI approval. The matching criteria and partial-match behavior are immediately clear.

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 clear usage context: at least one of company or model is required, matching is partial, and results are capped at 20. It does not explicitly name alternatives or state when not to use this tool, but the intended use case is unambiguous for an agent choosing between wheel-related lookups.

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?

With no annotations, the description carries the full burden and does it well: it discloses partial matching semantics, preference for available wheels, the not_found fallback, and the failure condition if fewer than 2 wheels are found. These are real behavioral traits beyond the basic compare action.

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 with no filler: the main purpose is front-loaded, and the matching/error details each earn their place. It is compact while carrying all essential operational information.

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?

Despite having no annotations and no output schema, the description is self-sufficient: it defines input expectations, matching behavior, the not_found output concept, and the failure condition. An agent has enough to select and invoke this tool 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 coverage is 100%, so the baseline is 3; the description adds value by explaining how the company/model inputs are interpreted (partial matching, availability preference) and the minimum-2-result requirement. This goes beyond the schema's bare '2 to 3 wheels' typing.

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 opens with a specific verb and resource ('Compare 2-3 wheels side by side') and specifies what the comparison yields (full specs and prices in every currency). This clearly differentiates it from sibling tools like search_wheels or get_wheel_price, which focus on discovery or a single price.

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 clearly establishes the intended context: use it when you need to compare two or three specific wheels and see specs/prices across currencies. It does not name alternatives or exclusions, but the side-by-side comparison intent is explicit and unambiguous.

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?

With no annotations, the description must disclose behavior on its own, and it does: it notes that available matches are preferred and that a discontinued wheel is returned with flags and last-known prices when it is the only match. This goes beyond a generic lookup description. It does not mention no-match behavior or response format, but the disclosed fallback behavior is meaningful.

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?

The description is two sentences with no filler. The first sentence front-loads the core purpose, and the second adds a valuable behavioral edge case. Every word contributes to the agent's understanding.

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?

For a two-parameter lookup with full schema coverage, the description covers the primary use case and an important fallback scenario. It does not state what happens when no match exists or describe the response shape, but the absence of an output schema and the low complexity make these gaps minor.

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 100%: both company and model are described as partial matches. The tool description adds no parameter-level detail beyond what the schema already provides, so the baseline of 3 is appropriate.

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 specific verb and resource: 'Get current pricing and specs for one wheel by company and model.' It explicitly limits the operation to a single wheel, which distinguishes it from sibling tools like search_wheels and compare_wheels. The purpose is immediately clear and not a restatement of the name.

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?

The description gives clear context for when to use the tool: when you need pricing and specs for a specific wheel identified by company and model. It does not explicitly name alternatives or exclusion conditions, but the 'one wheel' scope implies the boundary against search and compare tools.

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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It responsibly discloses the default availability filter, the effect of include_discontinued, the 20-result cap, and the 'Available' marker. It does not mention ordering or pagination, but the disclosed behaviors are meaningful and accurate.

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 short sentences, front-loaded with the core operation and immediately followed by the most important usage constraints. No wasted words or redundant restatement of parameter names.

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?

For a 12-parameter search tool with no output schema, the description covers the critical operational details: required filter, default availability scope, result cap, and result marker. It is missing some useful context such as result ordering and whether pagination exists, but the stated information is sufficient for correct first 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?

The input schema already documents every parameter (100% coverage), so the baseline is 3. The description adds value beyond the schema by stating that at least one filter is required and by clarifying the default behavior tied to include_discontinued. This is helpful semantic context not present in the schema itself.

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 specific verb and resource ('Search wheels') and conveys that it is a filter-driven catalog lookup. It does not explicitly differentiate from sibling tools like compare_wheels or get_wheel_price, but the name and framing make the distinction clear enough.

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 useful constraints: at least one filter is required, and the default scope excludes discontinued wheels unless include_discontinued is set. However, it provides no guidance on when to use this tool instead of the sibling tools or when a sibling would be more appropriate.

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