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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 and handles it well: it states nothing is bought, ordered, or paid, no quote is guaranteed, it is free, and it returns who receives the details, the consent wording, and how confirmation works. This is far more transparent than a generic 'describes' statement.

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: 'Read first.' front-loads the key instruction, followed by purpose, constraints, and return information with no filler. 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 documentation tool, the description fully covers what the tool does, what submit_enquiry does, what is not guaranteed, and what the tool returns. An agent has enough information to decide to call this first without opening any other tool.

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 carries no parameter semantics and the baseline of 4 applies. The description appropriately spends no space on parameters and focuses on behavior and return content.

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 a specific verb ('states plainly') and names the exact resource it documents ('what submit_enquiry does on Forensic Accountant Cost'), clearly distinguishing it from the sibling submit_enquiry tool. The title reinforces the informational nature: 'What you get: an ENQUIRY with a human...'

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' explicitly instructs the agent to consult this tool before acting, establishing it as a prerequisite to using submit_enquiry. It does not explicitly compare to enquiry_fields or state when not to use it, but the intended sequencing 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 must carry the burden of explaining behavior. It clearly discloses the informational content returned, but it does not mention read-only status, authentication, output format, or side effects. This is adequate but not fully transparent.

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, dense sentence that front-loads the core content and ends with practical cross-tool guidance. Every word earns its place with no redundancy.

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 metadata getter, the description is largely complete: it states the scope, enumerates the output fields, and connects to the submission workflow. However, without an output schema, it could more explicitly state the return shape (e.g., list or map).

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 there are no parameter semantics to document. The description appropriately focuses on the output, and the baseline of 4 applies since parameter documentation is not 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 names a specific resource ('the Forensic Accountant Cost enquiry') and enumerates exactly what is returned: key, label, type, required, help text, and allowed options. It is clearly distinct from submit_enquiry, though it does not explicitly differentiate itself from 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 description gives actionable usage guidance: after reading the fields, pass answers to submit_enquiry keyed by field key. This makes the intended workflow clear, though it does not explicitly state when not to use the tool or contrast it with enquiry_describe.

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 richly: it discloses that step 1 validates and returns a summary, consent line and token, that step 2 triggers an email with a click-through link before any provider sees the enquiry, and exactly what consent means. This is far beyond what a bare mutation description would provide.

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 critical disambiguation (NOT a purchase) is front-loaded, then the flow is laid out step by step. It is long, and the consent paragraph is duplicated verbatim from the schema, which is mild redundancy, but every other 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?

No output schema exists, so the description correctly explains what each step returns (summary, consent line, confirmation token) and the downstream email/link behavior. For a two-step consent-sensitive tool, nothing an agent needs 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 coverage is 100%, so the baseline is 3, but the description adds genuine meaning: it explains answers are keyed by field key from enquiry_fields, that consent must be true, and that confirmation is the token returned from step 1 after approval. It goes beyond restating the schema by embedding these in the workflow.

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 (submits) and resource (an enquiry to Forensic Accountant Cost) and immediately disambiguates it from a purchase or guaranteed quote. It also ties into the sibling enquiry_fields by naming the field key source, so an agent can tell it apart from 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?

Explicitly lays out a two-step flow with conditions: step 1 with answers+consent=true, step 2 only if the person agrees, with the same answers plus the confirmation token. The when-to-call-again rule and the dependency on the person's approval are both stated, 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.