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

Annotations are absent, so the description carries the full burden. It only says missing inputs fall back to defaults; it does not disclose whether the operation has side effects, requires auth, or what happens with invalid inputs. Worse, the listed inputs conflict with the actual schema, which is misleading behaviorally.

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 short, but it contains a typo ('calculator calculator') and a semicolon-delimited list of unrelated inputs that wastes space and misleads. It lacks a clean structure of purpose, usage, and behavior.

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 tool with four parameters, no output schema, and no annotations, the description should explain the calculation, its actual inputs, and expected output. It covers none of these, instead listing parameters contradicted by the schema, making the description fundamentally inadequate 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?

The schema documents all four parameters (age, country, pension, dependants) with 100% coverage, so the baseline is 3. But the description adds a list of phantom inputs (currency, monthly income, annual income, savings/deposit) that do not exist in the schema, and it fails to add any meaning for the real parameters, so it actually degrades parameter understanding.

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 says 'Run the Retirement visa threshold calculator calculator,' which names a specific verb and resource, so an agent can tell it invokes the calculator. However, it is redundant ('calculator calculator'), and it lists inputs (currency, monthly income, annual income, savings/deposit) that are not in the schema, muddying what the tool actually does.

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 no guidance on when to use calculate versus sibling tools such as calculator_describe or submit_enquiry. There is no when-to-use, when-not-to-use, or alternative selection 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?

With no annotations, the description carries the behavioral disclosure burden. It discloses the tool's informational nature and exactly what it covers (inputs, outputs, assumptions, tables), indicating a read-only describe operation with no calculation side effects. It does not mention return format or limitations, but for a pure metadata tool this is acceptable.

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?

Single sentence with no filler; it enumerates the three content areas in a logical order. It is slightly less direct because it lacks a main verb, but every word 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 describe tool with no output schema, the description gives enough to know what information will be returned. The main missing piece is an explicit statement that it returns a description rather than performing a calculation, though that is easily inferred from the name and sibling 'calculate.'

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 accepts zero parameters, so the schema is trivially complete and the baseline is 4. The description's mention of calculator inputs refers to the domain model being described, not to parameters of this tool, so no parameter documentation is needed. It adds no misleading parameter semantics.

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 as a documentation/metadata resource for the retirement visa threshold calculator, enumerating what it exposes: inputs with units/ranges/defaults, outputs, and underlying assumptions/tables. It does not contain an explicit verb like 'returns' or 'describes,' but the content and tool name make the purpose unambiguous and distinguish it from the sibling 'calculate' tool.

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?

No explicit when-to-use or when-not-to-use guidance is given. The context implies it should be read to understand calculator semantics before calling 'calculate,' but the description does not state this or name alternatives. This is implied usage at best.

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. It discloses useful behavioral context: nothing is bought, ordered, or paid; no quote is guaranteed; it is free; and it returns recipient details, consent wording, and confirmation behavior. It does not explicitly state that this tool itself has no side effects or describe its output format, but the 'describe' framing and zero parameters make the read-only nature reasonably clear.

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 concise and front-loaded with 'Read first', then states the core purpose and returns detail. It is slightly redundant with the title, which already conveys that this is about an enquiry rather than a purchase or guaranteed quote, but the description still earns its place by adding recipient, consent, and confirmation information.

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, no-output-schema informational tool, the description covers what the tool does and what it returns: an explanation of submit_enquiry, recipient information, consent wording, and confirmation mechanics. It is complete enough for an agent to invoke it correctly, though it could more strongly spell out the relationship to sibling tools such as calculator_describe and enquiry_fields.

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 shows an empty properties object, so there is nothing for the description to add about parameter meaning. The baseline of 4 applies because parameter semantics are fully moot and the schema coverage is complete.

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 clear, specific purpose: it explains what submit_enquiry does on the Retirement Visa Calculator, including that it starts an enquiry with human providers and returns explanation details. It distinguishes itself from submit_enquiry by being the descriptive counterpart, and the title reinforces that it is not a purchase or quote tool.

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' implies the tool should be used before interacting with submit_enquiry, and the description clarifies that it provides pre-submission context. However, it does not explicitly say when to use this tool versus siblings like calculator_describe or enquiry_fields, leaving the routing mostly to inference.

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 burden of explaining behavior. It clearly describes the output contents: key, label, type, required flag, help text, and allowed options. It does not explicitly state that this is a read-only operation, but for a zero-parameter metadata retrieval tool, the lack of side-effect disclosure is a minor gap. The description is adequate, not exceptional.

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, information-dense sentence that front-loads the core purpose and then lists the output attributes. Every clause earns its place, and the downstream usage pointer is included without unnecessary padding.

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 zero-parameter metadata tool with no output schema, the description is complete. It tells an agent exactly what information will be returned for each field, and how the keys relate to submit_enquiry. No critical information is missing 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?

The tool has zero parameters, so there is nothing to document. The description still adds useful semantic context by explaining that field keys are used to submit answers, which helps an agent connect this tool to sibling submit_enquiry. The baseline of 4 for zero-parameter tools 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 identifies what the tool provides: every field of the Retirement Visa Calculator enquiry, along with its metadata. It differentiates itself from sibling tools by focusing on field-level structure, and even references submit_enquiry as the downstream consumer of these field keys. However, it lacks an explicit verb like 'list' or 'get', and does not directly contrast with 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 useful usage context by stating that answers should be passed to submit_enquiry keyed by field key. This implies the tool should be used to discover the enquiry's fields before submitting. It does not explicitly discuss when to avoid this tool or how it differs from enquiry_describe, so it stops short of a 5.

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 present, so the description carries the full behavioral burden, and it does most of that work: it discloses validation, the returned summary/consent line/token, and the critical downstream side effect that the person must click an emailed link before any provider sees the data. It stops short of failure modes (invalid answers, expired token) or retry behaviour, so it is strong but not exhaustive.

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 core verb and the not-a-purchase caveat, then cleanly ordered step 1 / step 2. It loses a point for quoting the full consent paragraph verbatim, which is already present in the consent parameter's schema description, making that block redundant.

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 steps in to explain exactly what each call returns (summary, consent line, confirmation token) and what the person receives afterwards. For a two-phase, nested-object mutation with no annotations, nothing essential to correct invocation 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 baseline is 3, but the description adds real meaning: answers must be keyed by the field key from enquiry_fields, consent must be true, and confirmation is the token from step 1 used only on the second call. The procedural linkage across steps goes beyond what the schema descriptions convey.

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 to Retirement Visa Calculator') and immediately scopes it against related concepts ('NOT a purchase, NOT a guaranteed quote'). An agent can distinguish this from the calculate/calculator_describe/enquiry_describe siblings without opening any 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 procedure with the exact condition that selects each step: step 1 with answers+consent, step 2 'only if the person agrees' with the returned token. When-and-when-not guidance is fully specified rather than left to inference.

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