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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. It explains that the tool describes the enquiry flow, that no purchase/payment/guarantee is involved, that the service is free, and that it returns 'who receives the details, the consent wording, and how the person confirms.' This is rich, honest behavioral context for a description 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?

Two tight sentences, front-loaded with the 'Read first' directive and the core purpose. The description efficiently covers what the tool does, what it does not do, and what it returns, with 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 parameterless tool with no output schema, the description provides everything needed: the workflow position ('Read first'), the behavioral boundaries (free, no purchase, no guaranteed quote), and the return contents (recipient details, consent wording, confirmation method). An agent can confidently decide to invoke this tool and know what to expect.

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 an empty schema, so schema description coverage is 100% and there is nothing to document. The baseline for 0 parameters is 4; the description appropriately focuses on return content rather than nonexistent parameters.

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 verb and resource: it 'States plainly what submit_enquiry does' on Industrial Fridge Service. The title reinforces this by clarifying the user gets 'an ENQUIRY with a human' and explicitly ruling out 'a purchase, not a guaranteed quote.' This clearly differentiates it from the sibling tools submit_enquiry and enquiry_fields, which are the action and fields counterparts.

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 opens with the directive 'Read first,' which explicitly tells the agent when to use this tool: before interacting with the enquiry process. It also contrasts the tool's subject with what it is not ('Nothing is bought, ordered or paid; no quote is guaranteed'), giving clear usage framing. Though it does not name sibling tools, the 'Read first' instruction and the naming of submit_enquiry make the intended sequencing unambiguous.

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. It describes the content returned (field metadata) and implies a read-only lookup, but it does not explicitly state whether the operation is read-only, whether it makes any external calls, or what the exact response structure is. The description is informative but stops short of being fully transparent about the behavior beyond the data shape.

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, tightly packed with essential information. The first sentence lists the data attributes, and the second explains the integration with submit_enquiry. No filler or repetition; everything 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?

With no output schema, the description must convey what the tool returns, and it does: key, label, type, required, help text, and allowed options. It also explains the connection to submit_enquiry. However, it doesn't specify the exact format (e.g., array of objects, object keyed by field key) or mention how enquiry_describe relates, which leaves a small gap for an agent deciding between the 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?

The tool takes zero parameters, so the baseline is 4. The description adds contextual meaning by explaining that the field keys are meant to be used as keys when passing answers to submit_enquiry, which helps the agent understand the purpose of the returned keys even though there are no input parameters to document.

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 ('every field of the Industrial Fridge Service enquiry') and enumerates exactly what is included (key, label, type, required, help text, allowed options). It also distinguishes itself from submit_enquiry by explaining how the output relates to that tool, though it doesn't explicitly mention enquiry_describe or use an action verb like 'returns'.

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 second sentence gives direct usage guidance: 'Pass answers to submit_enquiry keyed by field key.' This tells the agent when to use this tool (before submitting) and how the output should be consumed. It doesn't explicitly contrast with enquiry_describe, but it provides clear context for the primary intended workflow.

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 and does so thoroughly. It discloses validation, the return of a summary/consent line/confirmation token, the two-call requirement, the post-submission email, and the condition that providers only see the enquiry after the person clicks the emailed link. This is exceptional transparency for a mutation-like action.

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 somewhat lengthy, but every sentence carries necessary operational or legal context. It is front-loaded with the core purpose and the 'NOT a purchase' clarifier, then flows logically through step 1, step 2, and the consent definition. 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?

There is no output schema, yet the description compensates by naming the returned artifacts (summary, consent line, confirmation token). It also covers the two-step lifecycle, the email link requirement, and provider visibility. For a multi-step interactive tool with nested parameters and no annotations, this description is fully adequate 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?

Schema description coverage is 100%, so baseline is 3. The description adds valuable semantics beyond the schema by explaining that answers are 'keyed by field key from enquiry_fields', that confirmation is the token from step 1, and by restating the exact consent text. This meaningfully enhances the bare parameter definitions.

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 verb ('submits an enquiry'), a specific resource ('Industrial Fridge Service'), and clearly disambiguates from related actions: 'NOT a purchase, NOT a guaranteed quote.' It also names the two-step nature, so an agent understands the tool's exact role even without knowing the sibling 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 explicit step-by-step guidance: when to call first (with answers and consent=true), what to do with the returned summary, and when to call again (only after agreement, with confirmation token). It also states the consent requirement verbatim, removing ambiguity about the condition for use.

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