Ukrainian license-plate and vehicle lookups: registry data, photos and reviews (trustcar.info).
Scored across 3 tools
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.
Look 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 provided, the description carries the full burden of behavioral disclosure. It implies a read-only lookup operation and names the external site, but it does not disclose potential error conditions, network dependencies, or rate limits. The description is adequate but does not go beyond basic expectations.
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 efficiently conveys purpose and output fields without redundancy. Every phrase 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 tool with one parameter and no output schema, the description fully covers what the tool does and what it returns (region, vehicle details, rating, reviews). The external source is specified. This is complete for a simple lookup operation.
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% for the single 'plate' parameter, which already includes a format description and example. The tool description adds no semantically new parameter information, keeping it at the baseline 3.
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 'Look up' and names the resource ('Ukrainian licence plate') and source ('trustcar.info'), listing the returned data (region, vehicle, rating, reviews). This clearly distinguishes it from siblings: recent_plates (listing) and post_review (writing).
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 does not explicitly state when to use this tool instead of recent_plates or post_review, but the read/lookup intent is clear from the verb and data fields. The context is sufficient, but it lacks explicit exclusion statements.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Post 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, the description carries the full burden and reveals several non-obvious behaviors: OAuth scope discovery, AI-agent-labelled output, separate section, and no impact on the human rating. This goes well beyond what the input schema provides.
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, front-loaded with the action, and followed by necessary authentication and behavioral caveats. Every sentence earns its place with 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?
For a simple 3-parameter write operation with no output schema, the description explains purpose, authentication, and the special output treatment. It does not mention response/error details, but the essential context for safe and correct use 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?
The input schema covers all three parameters (plate, rating, text) with descriptions, achieving 100% schema coverage. The description adds no additional parameter-level meaning, so a 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 begins with 'Post a licence-plate review', a specific verb+resource action. It clearly distinguishes from the sibling read tools (lookup_plate, recent_plates) by focusing on the write/submission operation.
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 states the authenticated-user requirement and OAuth 'write' scope, giving the context for when this tool applies. It does not explicitly name alternatives or when-not-to-use, but the sibling read tools make the write-vs-read distinction obvious.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List 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?
No annotations are provided, so the description carries the burden. It discloses the read-only nature via 'List' and adds the useful caveat that vehicle make/model are included only where known, setting expectations for the output.
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?
One sentence, front-loaded with the main action, and includes a relevant qualifier without waste.
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 list tool with a single optional parameter, the description provides enough context for invocation: it names the data source, the type of items, and an output detail. The absence of an output schema is mitigated by the straightforward nature of 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?
The schema already documents the limit parameter with range and default (100% coverage), so the description need not elaborate; it adds no additional meaning for 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 the specific verb 'List' with the resource 'recently active plates' on trustcar.info, clearly distinguishing from siblings lookup_plate and post_review.
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 listing recent plates but does not explicitly contrast with lookup_plate or post_review, leaving the when-to-use decision to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
lookup_platepost_reviewrecent_platesUkrainian business directory: companies, contacts and registry details (xtrust.info).
Ukrainian vessel registry: search by number/name/owner, full records, stats. No API key required.
Search hpt.su — Russian/EAEU vehicle compliance documents: type approvals, certificates, VIN lookup.
Look up a vehicle by license plate against official sources (make/model, year, status, restrictions)
Add one secure layer between your agents and this server.
