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

With no annotations, the description carries the behavior disclosure burden. It transparently notes the default-fallback behavior and lists the calculation outputs, but it does not mention side effects, output format, or how invalid inputs are handled. This is moderate transparency for a calculator 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 one concise sentence that front-loads the tool's purpose and ends with the key fallback behavior. The phrase 'calculator calculator' is a minor redundancy/typo that prevents a perfect score.

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 description is adequate for a simple calculation tool with all optional parameters and full schema coverage. However, since there is no output schema, the description could more explicitly state what the tool returns and in what shape.

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 parameter schema already documents all four inputs. The description adds some context by grouping them into calculation components, but it does not materially expand on the schema's meaning.

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 the tool runs the car import cost calculator and enumerates what it computes: customs value, import duty, import VAT, and DVLA first registration fee. This distinguishes it from the enquiry-related siblings, though it does not explicitly contrast it with calculator_describe.

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 is given on when to use this tool versus calculator_describe or the enquiry tools. The only usage hint is that missing inputs fall back to defaults, which is useful but not enough to guide tool selection.

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 transparency burden. It does convey that the tool returns explanatory content about the calculator rather than performing a calculation, but it does not explicitly state that the tool is read-only or side-effect-free. No contradiction exists.

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, compact sentence with no filler. It front-loads the primary content (inputs) and then efficiently lists outputs, assumptions, and tables, with every phrase contributing useful information.

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-parameter documentation tool with no output schema, this description is complete. It tells the agent exactly what information will be returned and leaves no critical gap for correct invocation.

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 properties and 100% schema description coverage, so there are no tool parameters that need clarification. The description's mention of calculator inputs refers to the domain model, not tool arguments, which is acceptable for a zero-parameter describe tool.

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 identifies the resource ('the Car import cost calculator') and the content of the tool's output: inputs with units/ranges/defaults, outputs, assumptions, and tables. This makes the describe-tool purpose clear, though it lacks an explicit verb like 'describes' and relies partly on the tool name and sibling context for 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 implies that this tool is useful when an agent needs to understand the calculator's inputs, outputs, or assumptions, but it gives no explicit guidance about when to use it versus siblings like calculate. There is no stated when-not-to-use or alternative-selection rule.

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 does meaningful transparency work: it states that nothing is bought, ordered, or paid, that no quote is guaranteed, and that the action is free. It also discloses what the tool returns. It does not explicitly say this describe tool itself has no side effects, but the verb 'states' and the listed return content strongly imply a non-mutating informational operation.

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: 'Read first' signals intended use immediately, the middle sentence delivers the core semantic distinction, and the final sentence enumerates the returned content. Every sentence adds distinct value without redundancy or fluff.

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 parameterless describe tool with no output schema and no annotations, the description covers the essential ground: what submit_enquiry does, the transactional caveats, and what the tool returns. It is slightly incomplete in not naming sibling tools or the output shape, but an agent can invoke this tool correctly with the information given.

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?

This tool has zero parameters and schema coverage is 100%, so there are no parameter semantics for the description to explain. Per the zero-parameter baseline, a 4 is appropriate; the description adds no parameter-specific detail because none is needed.

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 ('states plainly') and identifies the resource: what submit_enquiry does on Car Import Calculator. It also lists the concrete things the tool returns (who receives details, consent wording, confirmation). It stops short of explicitly distinguishing itself from all siblings like calculator_describe, so it is clear but not fully differentiated.

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 opening 'Read first' implies this tool is meant to be consulted before taking action, and the wording clarifies what will and won't happen when using submit_enquiry. However, it never explicitly names alternatives such as submit_enquiry, enquiry_fields, or calculator_describe, nor does it state when not to use this tool, so the usage context is implied rather than explicit.

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 full burden of behavioral disclosure. It implies a read-only metadata operation and describes the output contents, which is helpful. However, it does not explicitly state that the tool has no side effects, that the field definitions are static, or what happens if the enquiry is unavailable. This is acceptable but minimal.

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 concise sentences with no wasted words. The first sentence enumerates the returned field attributes, and the second connects the output to the submission workflow. Both sentences earn their 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 zero-parameter metadata tool, the description adequately covers what is returned and how the output should be used downstream. There is no output schema, so the description's enumeration of key, label, type, required, help text, and options is essential and sufficient. A small gap is the absence of any mention of related siblings or limitations, but the description is complete enough for correct invocation.

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 and the schema has 100% coverage of that, so there are no parameter semantics to explain. The description adds value by instructing that answers should be keyed by the field keys returned here, which is the main parameter-related guidance an agent needs.

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 (fields of the Car Import Calculator enquiry) and enumerates exactly what is included: key, label, type, required flag, help text, and allowed options. It does not use an explicit verb like 'returns' or 'lists', but it is unambiguous and is easily distinguished from submit_enquiry, which consumes those field keys, and from the other siblings that describe or calculate.

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 usage context: the keys returned by this tool are the ones to use when passing answers to submit_enquiry. It does not explicitly name when not to use this tool versus alternatives, but the connection to the submission workflow makes the intended use obvious.

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, and it excels. It reveals that step 1 only validates and returns a summary/consent/token, that submission requires the token, that an email with a clickable link is sent, and that providers see the enquiry only after the link is clicked. It also explains the exact meaning of consent, which is crucial for safe user interaction.

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 average but every sentence earns its place. It front-loads the core purpose and critical exclusions, then organizes the two-step workflow with clear numbering. The exact consent text and email behavior are important details that justify the length, and the structure makes it easy to follow.

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 no annotations and no output schema, this description is remarkably complete. It explains the entire flow from initial call to final submission, including intermediate return values, the required token, and the post-submission email step. An agent receiving this description would be able to invoke the tool correctly at both stages and handle user consent appropriately.

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 schema coverage is 100%, the description adds meaningful semantics beyond the property names. It specifies that 'answers' must be keyed by field keys from enquiry_fields, that 'consent' must be true and match the exact consent text, and that 'confirmation' is the token returned in step 1. This contextual mapping turns the schema's generic descriptions into a usable procedure.

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 opens with a clear specific verb and resource: 'Submits an enquiry to Car Import Calculator.' It immediately distinguishes the tool by stating what it is not ('NOT a purchase, NOT a guaranteed quote'), which helps separate it from siblings like calculate and calculator_describe. It also references enquiry_fields for the answer keys, anchoring its role in the broader flow.

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 an explicit two-step procedure: first call with answers and consent=true to get a confirmation token, then call again only after user approval. It clearly states when not to use it ('NOT a purchase, NOT a guaranteed quote') and specifies the consent condition verbatim, leaving no ambiguity about when and how to invoke the tool.

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