WhichTrim vehicle records

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

Beyond the readOnly, idempotent, and non-destructive annotations, the description discloses the output content (severity, park-outside and do-not-drive advisories, completion rate) and an important limitation about build ranges. This is especially valuable with no output schema present.

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 core behavior and output elements are front-loaded, and the crucial caveat is separated and emphasized with IMPORTANT. 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?

For a single-parameter read-only tool, everything needed for correct invocation is present: what the id represents, what the result includes, and the key interpretation caveat. The absence of an output schema is compensated by listing the returned information.

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 schema already documents the required id with an example, but the description adds meaning by framing the parameter as a model-year-level vehicle identifier and explaining why it cannot be treated as a VIN. That context helps an agent supply the correct id and interpret results.

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 action and resource: returning every NHTSA recall campaign for a model-year vehicle with severity, advisories, and completion rate. It also clarifies the scope by explicitly warning that it is not a per-VIN check, which distinguishes it from sibling tools like decode_vin.

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 makes the intended use clear (model-year-level recall campaigns) and gives an explicit when-not: it cannot determine whether one specific vehicle is affected. It names the alternative path (VIN check at nhtsa.gov/recalls), so an agent can route correctly.

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 signal read-only, idempotent, and non-destructive behavior, and the description does not contradict them. It adds behavioral context the annotations do not provide: what data the comparison includes and that starting price appears only when a press kit is on file.

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 a single, compact sentence that front-loads the comparison concept and lists decisive factors without padding. The lack of an explicit verb is a minor structural issue.

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 read-only comparison tool, the description tells the agent what will be compared and the press-kit caveat, and no output schema is expected, so the return-value burden is low. It covers the main facts needed to invoke the 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 for both parameters is 100% with examples and a required difference between a and b, so the structured data carries the semantic load. The description's content is about the output attributes, not the parameters, so it adds no value beyond the schema.

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 that the tool lays two model years/vehicles side by side and enumerates the comparison dimensions (fuel economy/EV range, Safety Signal grade, recalls, complaints, crash rating, starting price). This makes the resource and intended output clear and distinguishes it from single-vehicle tools like get_vehicle, though it never uses an explicit verb like 'compares.'

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 phrase 'figures that decide most purchases' implies a purchase-comparison use case, and the press-kit caveat adds a boundary. However, there is no explicit statement of when to choose this over get_vehicle or search_vehicles, and no exclusions.

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 readOnly, openWorld, idempotent, and non-destructive behavior. The description adds meaningful behavioral context by naming the external NHTSA database dependency and enumerating the fields it returns, including grade and recall campaigns. It does not contradict annotations.

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 one dense, front-loaded sentence that names the action, the external source, the join target, and the returned fields without waste. Every clause adds useful information for the agent.

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 single-parameter, non-destructive, idempotent tool with rich annotations, the description is nearly complete. It covers the input format, the external database, the join behavior, and the expected output fields. It does not describe failure behavior for invalid VINs or external service unavailability, but these are minor given the annotations.

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% and the single parameter 'vin' is described as 'A 17-character VIN.' The description repeats that format but adds no new semantic detail such as normalization, validation behavior, or case sensitivity. A baseline of 3 is appropriate because the schema already carries the parameter meaning.

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 uses a specific verb and resource: "Decode a 17-character VIN against NHTSA's vPIC database" and then explains the join to the WhichTrim record with a clear field list. This distinguishes it from sibling lookup tools like get_vehicle or lookup_bulletin by tying the VIN decode to NHTSA and WhichTrim data.

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 usage context is clear: this is the tool to use when a full VIN decode joined to WhichTrim model-year data is needed. It does not explicitly name alternatives or exclusion conditions, but the combination of NHTSA VPIC and WhichTrim join makes the selection unambiguous enough for an agent.

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 readOnly, idempotent and non-destructive, so the safety profile is covered. The description adds non-obvious behavioral context beyond that: coverage is limited to model years that have a press kit on file, which implicitly warns of empty results.

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 dense, front-loaded sentences with no filler; the default (no trim) behavior is stated first and the single-trim behavior second. Slightly run-on and clause-heavy, but every clause carries 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?

There is no output schema, so the description carries the full burden of describing return contents, and it does so in both modes (summary fields plus equipment/warranty for all trims; engine, dimensions, cargo, weights, brakes, wheels, tires for one). Nothing an agent needs in order to call it correctly is missing.

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, but the description adds meaning beyond the schema: naming a single trim switches the response from a side-by-side summary to a full specification sheet including the delta over the cheapest trim. That clarifies the consequence of the optional parameter rather than just restating its type.

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 (get every trim of one model year) and enumerates exactly what is returned: name, powertrain, drivetrain, MSRP, standard equipment and warranty. It also describes the second mode (single-trim spec sheet), so an agent can distinguish its behavior from get_vehicle or compare_vehicles without opening the 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?

Explains the branch condition clearly: omit `trim` for all trims side by side, pass it for one trim's full sheet. It also notes the prerequisite that only model years with a press kit on file have trims and points to search_vehicles to identify them. No explicit when-not-to-use versus siblings like compare_vehicles, so it falls short of a 5.

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, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds useful scope context by specifying 'one model year' and enumerating return contents, but does not disclose other behavioral traits such as output format, data freshness, or error behavior.

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?

A single dense sentence that front-loads the core purpose ('full published record for one model year') and then efficiently lists the return contents. There is no filler or redundant restatement of the tool name.

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?

With no output schema, the description carries the burden of explaining return values, and it enumerates the major record categories thoroughly. It could be slightly more explicit that the tool returns a single record object, but the singular 'record' and 'one model year' phrasing make this reasonably clear.

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%, and the id parameter already includes an example and a pointer to search_vehicles. The tool description adds no additional parameter-level meaning beyond what the schema provides, so the baseline score 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: retrieving the full published record for one model year, and enumerates the contained data categories. This clearly distinguishes get_vehicle from specialized siblings like check_recalls, lookup_bulletin, and lookup_trouble_code.

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 'full published record for one model year' framing implies use when comprehensive vehicle data is needed, and the id parameter schema points the agent to search_vehicles. However, the description itself gives no explicit when-to-use versus alternative guidance, such as 'use check_recalls for recalls only.'

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, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds useful behavioral context beyond annotations: it enumerates exactly what the response includes (NHTSA summary, model years, documents with verified PDF links, page URL) and discloses that the answer explicitly says it is not a recall.

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 efficiently structured: it opens with the resource and lookup method, provides illustrative examples, lists return contents, then closes with the recall caveat. Every sentence adds information; there is no fluff or repetition of the schema.

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?

With no output schema, the description fully carries the burden of explaining return values, and it does so explicitly: summary, model years, documents, PDF links, and page URL. It also covers the optional 'make' behavior and warns about the recall distinction. Nothing an agent needs to invoke it correctly is missing.

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%, so the baseline is 3. The description adds value beyond the schema by giving concrete number formats and examples, and by explaining the subtle behavior of the 'make' parameter: 'A number filed under several makes returns each without it.' This extra guidance helps agents use the parameters correctly.

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 lookup action with a clear resource: manufacturer service bulletins, campaigns, warranty extensions, or OTA notices found by their number. It gives concrete examples (Hyundai 9D6, Kia SC199) and explicitly distinguishes itself from recalls, which differentiates it from sibling tools like check_recalls.

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 clearly implies when to use the tool: when you have a bulletin, campaign, or document number from a letter or document. It also provides a when-not warning ('These are NOT safety recalls'), but it does not explicitly name alternative tools or direct the agent to check_recalls for recall 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?

Annotations already mark the tool as read-only, idempotent, and non-destructive, so the description only needs to add behavioral context. It does so by explaining exactly what data is returned: standard meaning, counts of bulletins and complaints, and affected model years. This goes beyond the annotations without contradicting them.

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 a single, information-dense sentence that front-loads the core purpose and then adds the unique value. The promotional aside 'the part no other source publishes' is slightly unnecessary, but the overall length is reasonable and the structure is clear.

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 one-parameter lookup with strong read-only and idempotent annotations, the description covers the main purpose and the shape of the result. It lacks an explicit output schema but effectively summarizes return dimensions. It could be more explicit about code formatting or validation, but the examples and schema description cover the essentials.

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% and the single parameter 'code' is already described as 'A trouble code, e.g. P0420.' The description reinforces this with additional examples and context, but it does not add significant new constraints or format guidance, so the schema can carry most of the semantic weight.

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 clearly states the tool's job: explain an OBD-II diagnostic trouble code and surface which vehicles' federal records mention it, with bulletin/complaint counts and model years. It is specific about the verb and resource, though it does not explicitly distinguish itself from sibling tools like lookup_bulletin.

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 implies that the tool is for looking up a trouble code's meaning and federal record mentions, but it gives no explicit guidance about when to prefer it over alternatives such as lookup_bulletin, decode_vin, or search_vehicles. There are no exclusions, prerequisites, or decision rules.

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, idempotentHint, and non-destructive behavior, so the description does not need to restate safety. It adds useful behavioral context by disclosing return contents: Safety Signal grade, recall count, page URL, and the vehicle id needed downstream.

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 with no fluff. The core purpose is front-loaded, return values are summarized, and the downstream usage note 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?

For a search tool with no output schema, the description sufficiently describes what the tool returns and why it matters. It could mention edge cases like empty results or pagination, but the essential invocation and output information is present.

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%, so the schema already documents query, year, and limit. The description loosely reinforces the query semantics with 'by make, model and/or year' but does not add meaningful detail beyond the schema.

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 action ('Find model years in the WhichTrim catalogue') and the search dimensions (make, model, year). It also distinguishes itself from siblings by positioning this as the entry point that supplies the vehicle id other tools need.

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 contextual guidance: use this first to obtain a vehicle id before using other tools. It does not explicitly name alternatives or exclusion cases, but the 'use this first' framing makes the intended workflow evident.

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