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

No annotations are provided, so the description carries the burden of behavioral disclosure. It explains that this tool returns information about the enquiry flow, and clarifies that the enquiry itself involves no payment or guaranteed quote. However, it stops short of explicitly stating that this tool itself is side-effect-free and only returns an explanation.

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 'Read first' and is reasonably concise. However, it repeats what the title already conveys ('not a purchase, not a guaranteed quote' appears in both), adding some redundancy without earning all of its wording.

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 descriptive tool with no output schema, the description serves as both the tool's contract and its output summary. It lists what the returned text will cover: who receives the details, the consent wording, and how confirmation happens. This is sufficient for an agent to 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 the input schema is empty, with full schema coverage. With no parameters to document, the description has no additional parameter semantics to provide, and a baseline of 4 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 clearly states the tool's function: it explains what submit_enquiry does on Gearbox Reconditioning Cost. It is specific about the verb ('States plainly') and the resource (submit_enquiry). It does not explicitly name or contrast with sibling tools, but the purpose is unmistakable.

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 phrase 'Read first' is an explicit usage instruction, telling the agent to invoke this tool before submit_enquiry. It gives clear context for when to use it, though it does not mention alternatives or exclusions explicitly.

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 states what is returned (field metadata) but does not explicitly say whether the operation is read-only, has side effects, or requires any permissions. Since this is a no-parameter query tool, one could assume it is safe, but the description does not confirm that. It also lacks details about the output format or potential variations (e.g., whether the list is static or dynamic). For a tool with zero annotation coverage, this is a significant gap.

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. It front-loads the core purpose ('Every field of the Gearbox Reconditioning Cost enquiry') and then lists the specific attributes, followed by a practical usage hint. There is no wasted wording, and the sentence is concise yet informative.

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 there is no output schema, the description explains what the tool returns (field metadata) and how to use it (pass answers keyed by field key). It covers the essential information an agent needs to call this tool and interpret its results. It does not mention edge cases like empty list behavior or errors, but for a simple field enumeration tool, it is reasonably complete. The absence of output schema is compensated by the explicit list of returned attributes.

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 schema already covers everything (100% coverage trivially). The baseline for 0 params is 4. The description adds value by explaining that the returned field keys are used as keys for submit_enquiry, which provides contextual meaning beyond the schema. No additional parameter semantics are 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 states a specific resource (the fields of the Gearbox Reconditioning Cost enquiry) and enumerates the attributes it exposes (key, label, type, required, help text, allowed options). This is clear and action-oriented, though it does not explicitly differentiate from the sibling enquiry_describe, which might describe the enquiry itself rather than its fields. The verb 'Every field' and the list of metadata make the purpose unambiguous.

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 practical usage instruction: 'Pass answers to submit_enquiry keyed by field key,' which tells the agent how to use the returned keys. It implies the tool should be called before submitting an enquiry, but it does not explicitly contrast with enquiry_describe or state when not to use it. The guidance is helpful but not comprehensive regarding alternatives.

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: it discloses that step 1 only validates and returns a summary/consent line/token, that step 2 actually submits, that an email confirmation link must be clicked before any provider sees the enquiry, and the exact scope of consent (up to three providers, emailed per firm, can stop). That is unusually rich behavioral disclosure for a mutation 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 key constraint ('NOT a purchase, NOT a guaranteed quote') and proceeds through the two steps in order with no filler. It loses a point for reproducing the full consent sentence verbatim, which already appears in the consent parameter's schema description.

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, and the description compensates by naming what step 1 returns (summary, consent line, confirmation token) and what step 2 triggers (submission plus a confirmation email). For a nested-object, two-phase tool, nothing an agent needs in order 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 description coverage is 100%, so the baseline is 3, but the description adds real meaning beyond the schema: 'answers' is keyed by field key from enquiry_fields, 'consent' must be true only on agreement, and 'confirmation' is the step-1 token used only after approval. The two-step role of the confirmation parameter is workflow semantics the schema cannot express.

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 ('Submits an enquiry to Gearbox Reconditioning Cost') and immediately bounds the scope with 'NOT a purchase, NOT a guaranteed quote'. It also cross-references the sibling tool enquiry_fields for the answer keys, so an agent can place it relative to its siblings without opening a schema.

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 gives an explicit two-step procedure with the exact condition gating each call: step 1 with answers+consent=true, step 2 only after the person agrees and with the confirmation token. The when-not (not a purchase, not a guaranteed quote) and the human-approval gate are stated outright.

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