CarsMultiverse — the recall record

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

With no annotations, the description carries the behavioral burden. It discloses the read-only nature ('Check', 'Returns'), the NHTSA vPIC source, the international scope, and notable output details such as do-not-drive and park-outside warnings and rewritten wording. It stops short of explaining what happens with missing or conflicting inputs.

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 front-loaded with the core action and contains no filler; every clause adds useful scope or output detail. It is slightly dense as a single long sentence, but the structure is acceptable given the tool's complexity.

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 no output schema and no annotations, the description covers the main input alternatives, source scope, and return content well. The main gap is not explicitly stating that the caller must provide either a VIN or make/model/year (and optionally plate+country), nor how the tool behaves if no lookup key is supplied.

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 67%, so the description must compensate. It adds meaning by explaining VIN decoding via NHTSA vPIC, the conditional make/model/year grouping, and that the plate parameter is Netherlands/Israel-only and returns open recalls for that exact vehicle. It does not explicitly state which parameter combinations are required.

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: 'Check one vehicle for safety recalls.' It clearly distinguishes this from siblings like get_recall_notice, list_registers, and search_recalls by focusing on a single vehicle and combining multiple international registers.

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: to check one vehicle by VIN or by make/model/year, with optional plate-based lookup. It does not explicitly name alternatives or state when not to use it, but the single-vehicle framing makes the intended use obvious.

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 of behavioral disclosure. It does add value by specifying the response contents (title, summary, register, dates, excerpt, citation) and implies a read-only operation via 'Get'. However, it does not mention error behavior, authentication needs, or what happens when the reference is invalid, which leaves some behavioral gaps.

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 wasted words. The first sentence states the action and return fields; the second states the input format. Information is front-loaded and every sentence 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 simple single-parameter retrieval tool with no output schema and no annotations, the description covers the core purpose, input format, and return contents. It is mostly complete, though it could add a note about when to use this instead of searching or listing, and what happens on invalid references.

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 fully documents the 'reference' parameter with examples. The description's 'Pass a recall number or a CarsMultiverse URL or path' largely restates the schema rather than adding new meaning. Baseline 3 is appropriate because the schema does the heavy lifting.

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 the specific verb 'Get' with the resource 'one recall notice kept by CarsMultiverse', and enumerates the returned fields (title, summary, register, dates, excerpt, citation). This clearly distinguishes it from siblings like list_registers and search_recalls, which handle listing and searching rather than direct retrieval.

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 explains how to call the tool ('Pass a recall number or a CarsMultiverse URL or path') but gives no guidance on when to use this tool versus the sibling tools search_recalls or list_registers. There is no explicit exclusion or alternative routing, so the agent must infer usage context from the tool name and input parameter.

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 are absent, so the description carries the disclosure burden. It adds useful context — that there are exactly seven registers and that entries include authorities and index pages — and the verb 'List' implies a read-only operation. However, it does not explicitly confirm side-effect freedom, describe return format, or address any error or pagination 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 front-loaded sentence: the verb and object appear immediately, and every subsequent phrase ('seven', 'authorities and index pages') adds concrete information. There is no filler, redundancy, or wasted verbiage.

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 trivial 0-parameter enumeration tool, the description states exactly what will be returned, which is the main thing an agent needs to invoke it correctly. Minor gaps are the unelaborated meaning of 'index pages' and the lack of an output schema, but the read-only nature is reasonably implied by 'List'.

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 tool declares zero parameters, so the baseline is 4; there are no arguments for the description to explain, and the input schema already fully covers the empty parameter set. The description instead usefully describes the output domain rather than parameters.

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 ('List') and a bounded resource ('the seven recall registers CarsMultiverse keeps'), and it states the returned content ('authorities and index pages'). The operation is clearly a fixed enumeration, which an agent can distinguish from get_recall_notice (single notice retrieval) and search_recalls (query-based search) without opening either sibling's 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?

No guidance is given on when to use this tool versus get_recall_notice or search_recalls, and no exclusions, prerequisites, or alternative conditions are mentioned. The only sense of 'when' must be inferred from the purpose statement, which is a minimal and implicit signal.

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?

No annotations are present, so the description carries the disclosure burden. It states the operation and output, and explicitly notes plain-language support in any language, but it does not mention error behavior, rate limits, data freshness, or read-only status—mostly inferable from 'Search' but not explicit.

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-load the core purpose and query modes, then give the output shape. There is no filler, tautology, or unnecessary repetition.

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 or annotations, the description usefully states return contents and query flexibility. It is slightly incomplete around limit behavior and error cases, but for a search tool it provides enough to call 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?

The schema already documents the query parameter well, and the description adds the 'any language' behavior and an example. However, it adds nothing about the limit parameter beyond the schema's numeric bounds, so at 50% schema coverage it only partially compensates for the gap.

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 action (search), concrete resource (vehicle safety recall notices across seven named registers), and query dimensions (recall number, make/model, plain-language). This differentiates it from siblings like get_recall_notice by emphasizing multi-register search and citation-oriented output.

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 context: use when you need to find recall notices by recall number, make/model, or problem description, and returns titles and URLs for citation. It does not explicitly name sibling tools or say when not to use them, but the supported query modes and output make the intended use obvious.

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