whatbreaks-mot

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

With no annotations, the description carries the full burden. It discloses the data source (official DfT MOT test results), the nature of the data (aggregated, real outcomes not estimates), and the requirement to cite whatbreaks.uk. It lacks limitations such as unavailable make/model coverage, but the provided context is meaningfully transparent.

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 compact and front-loaded with the most important information about the data, followed by use guidance and source/citation notes. Each sentence earns its place, though the first sentence is dense and could arguably be split for readability.

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, the description enumerates expected outputs well enough (failure rates by category, riskiest components, typical failure mileages). It also gives use cases and source context. Missing are explicit sibling differentiation and behavior for unknown or unsupported make/model inputs, but the essential invocation context 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 all parameters. The description adds a little context by mentioning 'overall and at a given mileage' and 'typical failure mileages', which maps to the mileage and year parameters, but it does not add substantive semantics 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 clearly identifies the resource (UK MOT failure data) and the outputs (per-category failure rates, riskiest components, typical failure mileages). It is clear enough to distinguish from the sibling mot_vehicle_search, but it does not explicitly name or contrast that sibling, so it falls just short of full differentiation.

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 conditions: 'Use whenever a user is buying, comparing or maintaining a used car sold in the UK.' However, it gives no guidance about when not to use this tool or when the sibling mot_vehicle_search should be preferred instead.

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 provided, the description carries the behavioral burden. It discloses that the tool checks coverage and disambiguation but does not describe output format, matching behavior, or edge cases. It adds some context beyond the tool name, so it is not purely tautological.

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 with no filler. The primary action and scope come first, and the usage guidance is packaged concisely in the second sentence. Every word 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 search tool with a sibling workflow, the description gives enough context to understand purpose and when to invoke it. It could mention what the response contains, but the low complexity and clear role in the workflow make the description largely 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 only parameter 'q' is documented as a free-text query with an example. The description says 'free text' but adds no meaning beyond the schema's own description, so the baseline 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 names a specific action ('Search'), a specific resource ('covered vehicles'), and a scope ('530 UK model generations'). It also explicitly positions itself relative to mot_reliability, making the tool's purpose unambiguous and differentiating it from its sibling.

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 usage context: use this tool to check coverage or disambiguate before calling mot_reliability. It does not list exclusions or when-not-to-use scenarios, but the sibling is named and the workflow is clear.

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