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

With no annotations, the description must carry the burden of behavioral disclosure. It identifies the topic (criteria and filters) but does not state whether the tool is read-only, what it returns, or any side effects or access considerations. It is not misleading, but it substantially under-discloses behavior.

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 short and front-loaded with the key noun phrase, which is good. However, the trailing 'by shape of cover comparison' is a dangling modifier and makes the sentence structurally ambiguous. It is concise but not cleanly composed.

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 tool with no output schema and no annotations, the description is the only meaningful signal, and it fails to state the tool's action, return value, or expected use. An agent reading this text cannot confidently determine what calling the tool will produce.

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 and schema description coverage is 100%, so there is no parameter-level information the description needs to add. The zero-parameter baseline applies and the description does not create any confusion about inputs.

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 the subject matter—criteria and filters for UK motor trade insurance comparison—and hints at its role relative to siblings like compare_options and compare_table. However, it is a noun phrase with no operative verb, so it never explicitly says the tool returns, describes, or compares anything. The phrase 'by shape of cover comparison' is also grammatically unclear.

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?

There is no guidance about when to use this tool versus alternatives such as compare_options or compare_table. The wording implies the tool concerns comparison criteria/filters, but no explicit when-to-use, exclusions, or alternative-routing guidance is provided.

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?

There are no annotations, so the description carries the full burden of behavioral disclosure. It describes the data content and optional filtering but never explicitly states that the tool is a read-only lookup, what the output format is, or what happens with invalid filters.

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 concise sentence with the resource front-loaded and the optional filtering included. The parenthetical list of fields makes it slightly dense, but overall it is efficiently worded.

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 optional-filter lookup with no output schema, the description conveys the domain, the fields of the options, and the filtering capability. However, it is incomplete on filter parameter semantics and does not explain the context for choosing this tool over sibling tools.

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 0%, so the description must explain filterKey and filterValue. It only mentions 'optionally filtered by a tag value,' which hints that filterValue is a tag value but leaves filterKey's role undefined and provides no valid values or key names.

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 resource ('options in the UK motor trade insurance') and lists the fields returned, but it lacks an explicit verb such as 'list' or 'retrieve.' It also does not clearly differentiate itself from siblings like compare_criteria or compare_table.

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 provided about when to use this tool versus its siblings. The phrase 'optionally filtered by a tag value' implies one usage mode, but there are no explicit conditions, exclusions, or references to alternative tools.

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 burden of behavioral disclosure, but it only describes content. It does not state that the operation is read-only, how results are returned, whether the optional 'option' parameter affects the result shape, or any side effects/pagination.

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 concise sentence that front-loads the core content. The phrase 'by shape of cover comparison' is slightly awkward and could be clearer, but there is no redundant 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?

For a low-complexity tool with one optional parameter and no output schema, the description adequately summarizes the returned table. However, it lacks details about the response format, the effect of the 'option' parameter, and any usage caveats, leaving modest gaps.

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 sole parameter is fully described in the schema ('one option's name, else all'), so schema coverage is 100% and baseline 3 applies. The description adds no parameter semantics beyond this, which is acceptable given the schema's clarity.

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 full comparison table of each option's value on every criterion for UK motor trade insurance. It distinguishes from siblings like compare_criteria and compare_options by emphasizing the complete table, though it lacks an explicit verb such as 'get' or 'list'.

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?

There is no guidance on when to choose this tool over compare_criteria, compare_options, or the enquiry tools. The phrase 'the full table' weakly implies use for complete data, but no explicit conditions or alternatives are stated.

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 transparency burden. It clearly states the action is non-transactional and free, and it discloses the returned content: recipient of details, consent wording, and confirmation method. It focuses on submit_enquiry's behavior rather than directly stating this tool is read-only, but the intent is clear.

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 imperative 'Read first.' It repeats some information from the title (not a purchase, no guaranteed quote) but remains efficient and easy 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?

For a zero-parameter informational tool with no output schema, the description adequately covers what it does, what it does not do, and what it returns. It could be slightly stronger on how it relates to the compare_* siblings, but the core context is 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?

The tool has zero parameters and 100% schema coverage, so parameter documentation is unnecessary; the baseline 4 applies. The description adds useful output-focused context instead.

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 specific verbs ('States', 'returns') to identify a meta-descriptive tool that explains submit_enquiry. It distinguishes this tool from an actual purchase or quote action by explicitly saying 'Nothing is bought, ordered or paid; no quote is guaranteed'.

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' is a clear ordering cue to call this tool before submit_enquiry, and the description clarifies what the tool explains. It does not explicitly list when-not-to-use conditions or contrast with compare_* siblings, so it falls just short of fully explicit routing.

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 burden of behavioral disclosure. It explains what the tool returns conceptually—fields with their metadata—but does not specify the exact return structure, whether the field list is static or dynamic, or whether there are any side effects. As a read-oriented metadata tool this is mostly adequate, but the absence of an explicit return format leaves some ambiguity.

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 deliver the core purpose and the practical next step with no filler. The description is front-loaded with the resource and content, then immediately provides usage guidance. Every sentence 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 zero-parameter, no-output-schema tool, the description is largely complete: it names the resource, enumerates the returned metadata, and explains how to use the result. It could be slightly more explicit about the output shape, but that is a minor gap given the tool's simplicity and the presence of clear workflow guidance.

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 fully described and the description has no parameter details to add. Per the rubric, a zero-parameter tool receives a baseline of 4, and the description appropriately avoids inventing parameter explanations.

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's resource: every field of the Motor Trade Insurance Compare enquiry, and details what information is provided (key, label, type, required, help text, allowed options). It distinguishes itself from submit_enquiry by explaining that answers should be passed there, though it does not explicitly compare itself to enquiry_describe or the comparison siblings.

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 clear usage context: use this tool to discover the field keys, then pass answers to submit_enquiry keyed by those keys. This implies the tool should be used before submitting an enquiry. It does not explicitly state when not to use it or name alternatives, but the workflow is clear enough for this simple metadata lookup.

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 burden and does so well. It discloses the two-step submission flow, the requirement to show the summary and consent line, and the side effect that a person receives an email link which must be clicked before any provider sees the enquiry.

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 long but every sentence serves a purpose: purpose, non-purchase warning, step 1 behavior, step 2 behavior, and the exact consent wording. It is front-loaded with the core purpose and follows a clear sequential structure.

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?

This is a complex two-step tool with no output schema and no annotations, yet the description tells the agent exactly what inputs to send, what to show the user, what to carry forward, what side effects occur, and what the consent condition is. Nothing essential for correct invocation 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 adds meaningful process-level semantics by explaining that confirmation is only used in the second call and that answers and consent must remain the same between steps, which is not fully captured by the schema's simple parameter descriptions.

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 action and resource: 'Submits an enquiry to Motor Trade Insurance Compare'. It also explicitly distinguishes this from other actions with 'NOT a purchase, NOT a guaranteed quote' and the title emphasizes 'two steps', which sets it apart clearly from the sibling comparison/enquiry-description tools.

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 detailed procedural guidance: Step 1 for validation and confirmation token, Step 2 only 'if the person agrees'. It also tells the agent when not to treat this as a purchase or quote, although it does not explicitly name sibling tools as alternatives.

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