MOT Check

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

Annotations already mark the tool read-only and non-destructive, and the description adds valuable context: it restricts to UK-registered vehicles, identifies the official DVSA data source, and discloses the derived mileage-decrease signal that can indicate clocking or error. This goes beyond the structured annotations and is genuinely useful.

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 well-structured and economical: it front-loads scope and data source, then explains the returned content and the extra mileage-consistency signal, and ends with concrete example queries. Every sentence earns its place with no filler.

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?

Although there is no output schema, the description enumerates what each returned test record contains and covers a wide range of likely user intents. For a single-parameter, read-only tool, this provides enough context for an agent to invoke it correctly without missing critical 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 input schema already fully describes the registration parameter, including case/spacing normalization and Great Britain/Northern Ireland limitations. The description reinforces the UK-only scope but does not add substantial parameter-level meaning beyond the schema, 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 operation: retrieving the full MOT test history for a UK vehicle, and enumerates what is included (date, result, mileage, advisories, minor defects, failures). This clearly distinguishes it from sibling tools by emphasizing historical test records rather than current status or general vehicle lookup.

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 explicit user-intent examples such as 'why did it fail its MOT', 'what were the advisories', 'has it been clocked', and 'is the mileage consistent', which tell an agent when to invoke the tool. It does not explicitly name siblings or state when not to use it, so it stops short of a full alternative-routing explanation.

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?

The annotations already mark the operation as read-only, idempotent, and non-destructive, so the burden on the description is lower. The description adds meaningful context: UK-registered vehicles only, official DVSA source, and the first_mot_not_due edge case explaining that expiry_date is the first MOT due date.

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 longer than minimal but each part earns its place: scope, source, returned data, special status behavior, and example queries. Some phrasing is slightly redundant, such as listing parallel user intents before the precise definition, but the structure is clear and front-loaded with the most important constraint.

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 lookup with no output schema, the description is sufficiently complete. It lists the key returned fields, explains the special first_mot_not_due status, constrains the input to UK registrations, and gives natural-language trigger examples, leaving no essential gap for an agent to invoke it 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 description coverage is 100%, and the registration parameter description already explains case-insensitivity, spaces, GB/NI-only, and the wr25vup example. The main description repeats 'UK-registered vehicles only' and includes wr25vup in a query example, but adds no significant semantic 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 clearly identifies the resource as the current MOT status of a UK vehicle, including expiry date, days remaining, and last test details. It avoids conflating with get_mot_history by emphasizing 'current' and 'most recent MOT test', though it does not explicitly name the sibling as an alternative.

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 explicit use-case examples such as 'when is my MOT due', 'has my MOT expired', and 'did it pass its last MOT', which tells an agent when to invoke it. It does not explicitly state when not to use it or which sibling should be used for full history, so it stops 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?

With readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false already present, the safety profile is covered. The description adds useful behavioral context beyond those annotations: it is restricted to UK-registered vehicles, it draws on DVSA/DVLA/HPI data, and it explains that MOT and tax status are part of the result. It does not describe error handling for invalid registrations, but that is a minor gap.

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 hard constraint 'UK-registered vehicles only,' then gives the purpose, then the invocation context. The example query list is long but earns its place because it helps an agent recognize varied phrasings for the same lookup.

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 is simple (one required parameter), the schema already documents the registration format, annotations cover the safety/state behavior, and the description enumerates all visible return fields. Nothing an agent needs to decide to call it or interpret its result 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 of 3 applies. The tool-level description reinforces the meaning by showing example registrations in natural-language queries, but it adds no format, normalization, or edge-case guidance beyond what the schema already provides.

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 names a specific verb ('look up') and resource ('UK number plate') and enumerates the exact returned attributes: make, model, colour, year, fuel type, age, MOT status/expiry, and tax status/due date. The final sentence's 'best first call for any general question' positions it as the general-purpose sibling to the dedicated MOT tools, so it distinguishes itself from get_mot_history and get_mot_status.

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 explicitly frames this as 'the best first call for any general question' and provides seven concrete example queries, which tells an agent when to reach for it. However, it never names the sibling tools or says when to prefer get_mot_status/get_mot_history, so exclusions and alternatives are left implicit.

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