site

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 clearly says nothing is bought, ordered, paid, or guaranteed, and it is free. It also states what the tool returns: who receives the details, the consent wording, and how the person confirms. This makes the tool's behavior transparent for an informational, zero-parameter 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 compact and front-loaded with the key usage instruction 'Read first.' Every sentence adds relevant information: what the tool describes, what it is not, and what it returns. There is no filler or repetition of schema content.

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 is complete. It says what the output covers — submit_enquiry's behavior, recipients, consent wording, and confirmation method — and gives enough context for the agent to know why it exists and when to read it.

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 is empty, so the baseline of 4 applies. There are no parameter semantics for the description to clarify, and the description does not need to compensate for any schema gap.

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 the tool's job directly: it explains what submit_enquiry does on Arizona Roof Replacement Cost. It distinguishes this 'describe' tool from the action tool submit_enquiry and makes plain that no purchase or guaranteed quote is involved. A reading agent can confidently tell this apart from its 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 opening 'Read first' is an explicit usage signal: this tool should be used before interacting with submit_enquiry, because it explains the enquiry process and consent details. It gives clear context for when to use it, though it does not explicitly name alternatives or state when not to use 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?

No annotations exist, so the description carries the full behavioral burden. It does well by enumerating the exact returned attributes and making the mental model explicit: the field keys returned here are the keys for submit_enquiry. It does not discuss output shape or pagination, but for a parameterless read-only metadata query these are minor omissions.

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 concise sentences with no filler. The first sentence front-loads exactly what the tool returns, and the second adds the actionable usage instruction. Every clause 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, parameterless metadata tool with no output schema, the description is complete: it specifies the scope of results, the fields available, and how the result should be used with submit_enquiry. There are no required preprocessing steps or hidden constraints to disclose.

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?

There are no parameters, so the schema fully covers input semantics; the description has no parameter behavior to add. The only extra semantic guidance, the instruction to key submit_enquiry answers by field key, relates to output usage rather than 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 clearly identifies the tool's resource and content: every field of the Arizona Roof Replacement Cost enquiry, including key, label, type, required flag, help text, and options. It is specific about what is returned, but it never explicitly contrasts itself with the sibling enquiry_describe, so the differentiation is left largely to the name and title.

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 guidance: pass answers to submit_enquiry keyed by field key, which tells the agent why it would fetch this data. It does not formally state when to use this tool instead of enquiry_describe, but the submission-related instruction provides clear practical context.

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 and meets it: it discloses validation behavior, what Step 1 returns (summary, consent line, token), the required human approval gate, and the post-submission email-link verification before any provider sees the data. It even reproduces the exact consent meaning. This is unusually rich behavioral context 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?

Long, but every sentence earns its place for a two-step, human-consent gated flow, and it is front-loaded with the 'not a purchase' disambiguation before the step detail. Slightly verbose in restating the consent text twice, but justified by the consent requirement.

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 describing exactly what Step 1 returns and what happens after Step 2 (email link the person must click). Combined with the nested-object answers parameter, an agent has everything needed to drive the flow correctly.

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, but the description adds real meaning: 'answers' is keyed by field key from enquiry_fields, 'consent=true' is tied to specific language the person must have agreed to, and 'confirmation' is the token from Step 1 used only after approval. This goes beyond the schema's field-level wording.

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?

States a specific verb and resource ('submits an enquiry'), names the target ('Arizona Roof Replacement Cost'), and immediately disambiguates with 'NOT a purchase, NOT a guaranteed quote.' An agent can distinguish this from the sibling discovery tools (enquiry_describe, enquiry_fields) 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?

Gives an explicit two-step protocol: Step 1 with answers+consent=true, show the person the summary and consent line; Step 2 'only if the person agrees' with the token. The condition that selects each call is spelled out, leaving nothing to inference.

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