pricemyrepair-mcp

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

No annotations are provided, so the description carries the full behavioral burden. It does disclose useful traits: it returns the full table, covers every repair in the price matrix, uses GBP, and is a single-call operation. However, it does not describe the output format, error behavior, or any access requirements, leaving some ambiguity for an agent.

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 primary purpose is front-loaded, and the usage guidance is included without bloat, making it easy for an agent to parse quickly.

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 one-parameter read tool, the description covers the essential context: what data is returned, the currency/region, the scope ('every repair'), and the general use case. It does not describe the return structure, but given the low parameter complexity and the schema's completeness, this is only a minor gap.

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 sole parameter 'model' is already well-documented with an example and the 'uk-average' special value. The description adds mild context by saying the costs are 'for one car model', but it does not meaningfully extend the parameter semantics 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 states a specific action ('Get the full table'), a precise resource ('typical UK repair and servicing costs (GBP) for one car model'), and a distinguishing scope ('every repair the price matrix covers, in one call'). This makes it easy to tell apart from the sibling get_repair_cost, which presumably returns a single repair cost.

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?

It explicitly says when to use the tool: 'when the user asks what a particular car costs to maintain or repair in general.' This provides clear context, though it does not explicitly name sibling tools or state when not to use it, so it falls just 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 no annotations provided, the description carries the full burden of behavioral disclosure. It clearly states that the tool returns independent-garage and dealer estimates plus a link to the source page, and it discloses scope limitations ('Covers 15 popular UK models plus a UK-average figure'). It could additionally note error or unknown-input behavior, but the main behavior and outputs are well explained.

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 concise and front-loaded, opening with the core purpose, then returning to what the agent receives, then usage and coverage. Every sentence adds value, and there is no redundant or filler content.

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 two-parameter lookup with no output schema, the description covers the main purpose, return content, coverage limits, example usage, and discovery tools. It does not fully specify the exact output structure or error behavior, and it misses an explicit nod to get_model_costs, but the essential information for calling the tool correctly 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 fully documents both parameters. The description adds useful context about coverage (15 models and UK-average) and gives examples, but it does not substantially add meaning beyond the parameter descriptions already present. Baseline 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 and resource: 'Get the typical UK cost range (GBP) for a specific car repair on a specific car model.' It clearly states what the tool does and differentiates from the list_* siblings by indicating it returns costs rather than listing available models/repairs. However, it does not explicitly distinguish itself from the sibling get_model_costs, so it is not a full 5.

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 a concrete usage example ('how much is a clutch on a Ford Fiesta in the UK') and explicitly directs users to list_models and list_repairs to discover available data. The schema also guides using 'uk-average' for unknown models. It lacks an explicit 'when not to use this tool' or comparison with get_model_costs, but the guidance is otherwise clear.

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 description discloses a filtering behavior: it only lists models that have per-model UK repair prices, not all car models. However, with no annotations and no output schema, it does not state return format, ordering, pagination, or whether the output can be used as input to sibling tools. The disclosure is minimal but non-contradictory.

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 with no filler. The condition 'has per-model UK repair prices for' is front-loaded and immediately clarifies scope. It is appropriately concise.

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 zero-param listing tool with no output schema, the description conveys the core purpose and filtering condition. It is somewhat sparse about the nature of the returned model list (e.g., whether it returns identifiers suitable for downstream calls), but given the simplicity, it is largely complete. The absence of links to sibling tools is a minor gap.

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 has zero parameters, so the description needs no parameter guidance. The baseline for 0-param tools is 4; the description does not need to add parameter semantics.

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' with a clear resource 'car models' and a scope condition ('has per-model UK repair prices for'). This differentiates it from siblings like list_repairs and get_model_costs, which list repairs or fetch costs respectively. The purpose is immediately understandable.

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 on when to use this tool versus alternatives is provided. It does not mention that this is the discovery step before calling get_model_costs or get_repair_cost, nor does it state any exclusions. An agent is left to infer from the tool name and sibling names.

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 provided, so the description carries the behavioral disclosure burden. It makes clear this is a read-only listing operation and tells the agent what output content to expect: repair/servicing jobs and the page each is documented on. Minor details like ordering or exact output shape are absent, but for a parameterless list tool this is reasonably 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?

A single, front-loaded sentence states the action, the resource, the scope, and the key output attribute. There is no filler or repetition of the tool name.

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, parameterless tool with no output schema, the description adequately explains what is returned and the scope of the data. It does not explicitly mention edge cases, ordering, or how to handle empty results, but these are not critical for this kind of list 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?

The tool has zero parameters, so the input schema is trivially complete. Per the baseline for zero-parameter tools, the description does not need to add parameter details; it adds helpful contextual scoping with 'UK price ranges' and page references.

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 specific resource ('the repair and servicing jobs PriceMyRepair has UK price ranges for'), and adds the useful detail that each entry includes the page it is documented on. This clearly differentiates it from the sibling tools, which deal with model costs, repair costs, and model listings.

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 this is the tool to use when you need an enumeration of repair/servicing jobs with UK price ranges and their source pages. However, it does not explicitly contrast it with siblings like get_repair_cost or list_models, leaving some routing to inference.

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