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

With no annotations, the description carries the burden and succeeds: it discloses that nothing is bought/ordered/paid, no quote is guaranteed, it is free, and it describes the returned content (recipients, consent wording, confirmation path). This is strong behavioral context beyond the bare tool name.

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 packs the key facts into a few tight sentences without repetition or filler. Every sentence adds distinct value.

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 definition fully covers what the tool does, which sibling it supports, what it does not do, and what information it returns. No essential context 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?

The input schema has zero parameters, so there is no parameter burden for the description to carry. The description nonetheless adds relevant context about what the tool explains/returns, so baseline 4 applies.

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 a specific function: it 'states plainly what submit_enquiry does' — starting an enquiry with human providers — and distinguishes itself from purchase/quote outcomes. As a describe-type tool, it clearly differentiates from the sibling submit_enquiry by being the read-first explanation.

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?

'Read first' gives an explicit usage cue to consult this before acting, and the description frames it as the explanatory counterpart to submit_enquiry. It doesn't explicitly contrast with enquiry_fields or state when not to use it, but the context is clear.

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 explaining behavior. It does disclose the nature of the return data and the conditional presence of options. However, it does not describe the response format, whether the data is static, or any access considerations, leaving some behavioral context implicit.

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, no filler. The first sentence front-loads what the tool returns, and the second adds actionable integration 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 introspection tool with no output schema, the description thoroughly covers the return content and how to apply it. It names the relevant sibling submit_enquiry and explains the keying contract. Nothing critical is missing given the tool's low complexity.

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 zero parameters, so the schema is trivially complete. The description appropriately focuses on the output rather than parameters, and no 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 verb and resource: it lists every field of the Texas HVAC Replacement Cost enquiry, enumerating the exact attributes returned (key, label, type, required, help text, options). This clearly distinguishes it from submit_enquiry, which consumes answers, and from the vague sibling enquiry_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?

The second sentence gives clear practical guidance: pass answers to submit_enquiry keyed by field key. This tells an agent how to use the output correctly, though it does not explicitly state when to choose this tool over enquiry_describe or mention exclusions.

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 it validates rather than purchases, what step 1 returns (summary, consent line, token), that the person must be shown the summary and consent line before proceeding, that a verification email must be clicked before any provider sees the data, and the exact consent wording.

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?

Front-loaded with the critical 'not a purchase' framing and then sequenced by step, so the flow is easy to follow. The verbatim consent sentence is repeated inside the schema description, which is mild redundancy in an otherwise tight 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 explaining exactly what step 1 returns and what step 2 triggers downstream (submission and the verification email). An agent has everything needed to drive both steps 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: 'consent' is tied to the verbatim agreement text, 'confirmation' is explicitly the step-1 token reused in step 2, and 'answers' is described as keyed by field key from enquiry_fields.

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+resource ('Submits an enquiry') and immediately scopes what it is not ('NOT a purchase, NOT a guaranteed quote'). It is clearly distinguishable from the sibling helpers enquiry_fields and enquiry_describe, which it references as a data source.

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 two-step protocol is spelled out: step 1 with answers + consent=true to validate and obtain a token, step 2 only if the person agrees, with the same answers plus the token. It also names enquiry_fields as the source of field keys, so the agent knows the prerequisite call.

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