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

No annotations are provided, so the description carries the full burden. It transparently discloses the output behavior: a price range built from real listings, an optional plain verdict, and the canonical-format requirements for make/model. It does not mention potential failure modes or data freshness, but the disclosure is strong for a read-only comparison 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 front-loaded with the core purpose, then explains output, scope, and parameter formatting in a logical order. Every sentence adds value; examples are concrete and the IMPORTANT caution is placed near the end without bloating the text.

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 the tool has no output schema, the description adequately covers return semantics (low/typical/high range and fair/above/below verdict). It also handles the tricky naming prerequisite. It stops short of describing exact output structure or error behavior for unknown or misspelled models, but these are minor gaps for this query-style 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 description adds meaningful semantics beyond the schema by explaining why asking_price is passed, and by emphasizing canonical Latin make/model formats with concrete examples ('Renault' not 'VW', 'Clio' not 'Clio dCi'). This materially helps the agent invoke the tool correctly.

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 clear verb-resource pair: 'Check whether a car's asking price is fair for the Portuguese market.' It further specifies the inputs (make, model, year, mileage), what is returned (low/typical/high range plus a verdict), and distinguishes itself from inventory tools by noting it works for ANY car, including listings found elsewhere.

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—when evaluating a car's price before purchase—and explicitly directs the agent to call list_makes_models first if unsure of spelling. It does not explicitly enumerate exclusions for sibling tools like search_inventory or get_car, but the 'ANY car' statement provides enough distinction.

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 warns that submitting shares the user's contact details per the privacy policy, which is a critical side effect. It also highlights the strict consent requirement. It could additionally mention what happens after submission, but the essential behavioral traits are 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 three sentences with no filler. The primary purpose is front-loaded, followed by precise invocation conditions and a privacy consequence. Every sentence earns its place and the structure is easy for an agent to parse.

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 the absence of annotations and output schema, the description is remarkably complete for correct invocation. It covers the purpose, the exact preconditions, the consent flag semantics, and the privacy implication. Nothing needed to decide whether and how to call the tool 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?

The schema already describes all parameters with 100% coverage, so the baseline is 3. The description adds meaningful context by tying the parameters to user conditions: the name must be the user's own, contact must be a phone/email, and consent must be true only when the user explicitly agrees. This reinforces and clarifies the schema without redundancy.

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 a specific verb and resource ('Send a contact request to TransparentCars') and clearly states the intended outcome ('so a human gets in touch'). This clearly distinguishes the tool from the sibling search and information tools, which serve different purposes.

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 states when to use the tool: only when the user explicitly asks to be contacted, provides their own name and phone/email, AND agrees to share those details. It even instructs to pass consent=true only under that condition, providing clear and unambiguous usage guidance with exclusions.

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 exist, so the description carries the full burden. It usefully discloses the 'floor' nature, the annual IUC tax, geographic scope, and required inputs, but it does not explain output format, calculation assumptions, or how optional inputs such as month and used affect the estimate.

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 tight sentences: the core purpose and scope are front-loaded, and the use-case sentence earns its place. There is no filler or repetition.

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 tax-estimation tool with no output schema and no annotations, the description should say more about what is returned and which factors drive the estimate. It communicates the domain well but leaves the 'plain running-cost framing' and output details vague.

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 description only renames the four required parameters in plain terms and adds no meaning beyond the schema, such as how month refines the CO₂ regime or how the used flag affects the result.

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 opening phrase 'Estimate a car's yearly running-cost floor in Portugal' is a specific verb + resource, and naming the IUC road tax anchors the tool precisely. It is clearly distinct from siblings like check_fair_price and search_inventory, which concern price and availability rather than ownership running costs.

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 context: 'Use this to help a buyer budget the true cost of keeping a car, beyond the sticker price,' and lists the required inputs. It stops short of a 5 because it does not state when not to use the tool or name a specific alternative tool.

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 exist, so the description carries the full burden. It conveys that this is a read-only retrieval operation ('Get full details') and gives the identifier source, but it does not disclose response structure, error behavior, or any side effects. 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 well-structured sentence with no filler. It front-loads the core purpose, then adds the essential slug context and an example. Every word contributes.

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 get-by-slug tool, the description is mostly sufficient: it names the resource, input, and source. However, since there is no output schema, the phrase 'full details' is somewhat vague and does not fully specify what information will be returned.

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 description adds value beyond the schema by giving a concrete slug format example ('renault-clio-2016') and reaffirming where the slug comes from, which helps an agent construct valid input.

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 ('Get') and resource ('full details for one car in TransparentCars stock'), and clearly identifies the input mechanism (slug from a search_inventory link). This distinguishes it from sibling tools like search_inventory, which lists inventory, and cost_to_own or check_fair_price, which cover different concerns.

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: use this after obtaining a slug from a search_inventory result. It does not explicitly name alternatives or state when not to use it, but the workflow implication is strong and unambiguous enough for an agent to select it correctly.

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 burden of behavioral disclosure. It explains the dual behavior (brands vs. models), emphasizes canonical/exact string output, and communicates that results are meant to be reused verbatim. It stops short of explicitly stating that the operation is read-only or describing the exact return format, but for a listing tool this is largely sufficient.

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 long, front-loaded with the core purpose, and then flows naturally into usage conditions and parameter behavior. Every sentence earns its place, with no filler or repetition of what the schema already states.

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 tool with one optional parameter and no output schema, the description is complete: it explains the two modes, states how results should be used, and names the consumer tools. Nothing needed to call it correctly 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 coverage is 100%, so the baseline is 3. The description goes beyond the schema by clarifying that the returned values are canonical and 'exact strings' that must be passed verbatim, which reinforces why this parameter matters and how the output should be consumed. This adds meaningful context beyond the schema definition.

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 tool as a reference lookup for canonical car makes and models, explicitly naming the exact strings that other tools expect. It distinguishes itself from sibling tools by positioning itself as the data source to use before search_inventory or check_fair_price.

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, actionable guidance: call this FIRST or whenever unsure of spelling, and pass returned values verbatim into other tools. It also clarifies the two usage modes, with and without the make parameter, leaving no ambiguity about when and how to invoke it.

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, the description carries the full behavioral disclosure burden. It reveals return contents, requires canonical Latin make/model spelling, warns against putting fuel in the model field, and instructs to call list_makes_models for uncertain spellings. It does not cover rate limits, errors, or no-match behavior, but the key operational quirks are disclosed.

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 purpose is front-loaded, the critical formatting instructions are grouped under 'IMPORTANT', and the inline examples are useful rather than filler. It is longer than a minimal definition, but every sentence serves either scoping or input-formatting, so the length is justified.

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 7-parameter search with no output schema and no annotations, the description covers the core call envelope: what inventory is searched, how to format make/model correctly, what filters exist, and what the response will contain. It omits mention of min_year and limit in the narrative, and does not address sorting or pagination, but the schema supplies the remaining parameter detail.

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?

Although the input schema already describes all parameters, the description adds substantial meaning beyond those labels: concrete examples and anti-examples ('Mercedes-Benz' not 'Mercedes', 'Golf' not 'Golf 1.5 TSI') and a warning to place fuel only in the fuel field. This is exactly the kind of practical clarification schema descriptions rarely provide.

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 and resource: 'Search TransparentCars' current stock of budget used cars in Portugal.' It also lists the filter dimensions and the returned fields (price, year, mileage, link), which distinguishes it from sibling tools like get_car, check_fair_price, or list_makes_models.

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 establishes when to use this tool—searching current inventory—and explicitly points to list_makes_models as a fallback when spelling is uncertain. It does not explicitly exclude other siblings like check_fair_price or get_car, but the inventory-search context and the named alternative give solid practical guidance.

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