Ukrainian license-plate and vehicle lookups: registry data, photos and reviews (trustcar.info).
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Average 4/5 across 3 of 3 tools scored.
Each tool targets a distinct operation: lookup retrieves plate data, post_review creates a review, recent_plates lists plates. No overlapping purposes or ambiguous boundaries.
Two tools follow verb_noun pattern (lookup_plate, post_review), but recent_plates is adjective_noun, breaking the pattern. This is a minor deviation and names remain clear.
Three tools is a well-scoped set for a plate-lookup service, covering lookup, review submission, and recent activity without unnecessary extras.
Core workflow (lookup, review, browse) is covered. Missing update/delete review or user's review management, but these are likely outside the service's primary purpose.
lookup_plateAInspectLook up a Ukrainian licence plate on trustcar.info: region, vehicle (make/model/year/fuel), rating and recent reviews.
| Name | Required | Description | Default |
|---|---|---|---|
| plate | Yes | Plate number, e.g. AA1234BB |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of disclosure. It makes the read-only nature of a lookup clear and specifies the output fields, but it does not mention potential network dependence, failure modes, rate limits, or data freshness. This is adequate but not rich.
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, front-loaded sentence that states the action, resource, and expected output without any redundant words or over-specification.
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 a single required parameter, no output schema, and no annotations, the description covers the purpose and the data returned sufficiently. It does not explicitly address error handling or external dependencies, but for a simple lookup tool it is nearly complete.
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 schema already includes an example ('AA1234BB'). The description adds only the 'Ukrainian' context, not parameter format or additional semantics, so the baseline of 3 applies.
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 ('Look up') and names the exact resource ('Ukrainian licence plate on trustcar.info'), followed by the data returned (region, vehicle, rating, reviews). This clearly distinguishes it from sibling tools like post_review and recent_plates.
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 usage for looking up a single plate but does not explicitly state when to use this tool versus alternatives, nor does it mention exclusions. Sibling names suggest other functions, but no direct guidance is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
post_reviewAInspectPost a licence-plate review on behalf of the authenticated user. Requires an OAuth access token with the "write" scope (discover via /.well-known/oauth-protected-resource). The review is labelled AI-agent-generated, shown in a separate section, and does NOT affect the human rating.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Review text (min 3 chars) | |
| plate | Yes | Plate number, e.g. AA1234BB | |
| rating | Yes | Star rating 1-5 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and does so excellently. It discloses the OAuth requirement, the AI-agent-generated labeling, the separate section placement, and the crucial fact that the review does not affect the human rating. This goes far beyond a generic 'post review' statement.
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 three concise sentences, each earning its place: purpose/auth, label/placement, and rating-effect caveat. It is front-loaded with the core action and immediately provides essential context without wordiness.
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 three required parameters, no output schema, and no annotations, this description covers the critical contextual aspects: authentication prerequisites, discovery method, behavioral labeling, and the non-impact on human ratings. It leaves no major ambiguity for an agent attempting to invoke the tool.
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 schema already describes each parameter ('min 3 chars', 'e.g. AA1234BB', 'Star rating 1-5'), and the description adds no additional parameter-level semantics beyond those in the schema. It does not hinder understanding, but it does not elevate it either.
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 ('Post') and resource ('licence-plate review') with clear scope ('on behalf of the authenticated user'). It is immediately distinguished from sibling tools like lookup_plate and recent_plates, which are read-only lookups.
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 provides clear context on when the tool is appropriate: it requires an OAuth access token with 'write' scope and explains how to discover it. While it does not explicitly name alternatives or exclusions, the stark contrast with read-oriented siblings makes the use case unambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
recent_platesAInspectList recently active plates on trustcar.info (with vehicle make/model where known).
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | 1-50, default 12 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It honestly notes that vehicle make/model is included only 'where known', which is a useful data-quality caveat. However, it does not disclose whether the operation is read-only (implied by 'list'), how results are sorted, or pagination behavior. Minor gaps for a simple list tool.
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 front-loaded sentence with no filler words. It efficiently conveys the tool's purpose and a key caveat about data completeness.
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 tool with one optional parameter and no output schema, the description covers the core purpose and notes the make/model caveat. However, it omits response structure, sorting order, or the time window for 'recently active', leaving some ambiguity. Adequate but not fully complete.
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 fully documents the only parameter 'limit' with range (1-50) and default (12), covering 100% of parameter semantics. The description adds no additional information about the parameter, 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 uses a specific verb ('List') and resource ('recently active plates'), clearly distinguishing it from siblings like lookup_plate (singular lookup) and post_review (mutation). The phrase 'on trustcar.info' adds context, making the tool's purpose unambiguous.
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 usage for retrieving recent plates but does not explicitly state when to use this tool versus alternatives. It lacks exclusions, prerequisites, or alternative recommendations, leaving the agent to infer from sibling names.
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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