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

With no annotations, the description must carry the burden. It does disclose the primary behavior - returning the next question or the final verdict - but it does not say whether invoking this tool mutates/persists state, whether repeated calls with the same inputs are idempotent, or what happens on invalid choice indices.

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?

One sentence, front-loaded with the action, and every clause earns its place by linking inputs to outputs. No filler or repetition.

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 low-complexity 2-parameter tool this is nearly complete: it covers inputs and the two possible outcomes. The main gap is that there is no output schema and the shape of the 'final verdict' or 'next question' is not described, but the expected interaction pattern is clear.

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 0%, so the description must compensate. It usefully reinterprets `question` as a question id and `choice` as the 0-based option index, which adds a little meaning beyond the raw schema. However, it leaves ambiguity about how the id is obtained (e.g., from checker_start) and whether the choice index references the current question's option list.

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 verb ('return'), the resource (next question / final verdict), and the inputs (question id + choice index), and the title reinforces the one-step advancement action. It also distinguishes itself from siblings like checker_start and checker_tree, which clearly handle starting and viewing the tree rather than stepping through it.

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 flow is clear: given a question and a chosen option, call this tool to progress to the next step. It does not explicitly state when NOT to use it or name checker_start/checker_tree as alternatives, but the context (answering a question to get the next step) is unambiguous enough that an agent can infer the right moment to invoke 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 are provided, so the description carries the full burden of disclosing behavior. It only states that the tool is the first question; it does not disclose whether the tool is read-only, what it returns, whether it resets or starts state, or whether it has any side effects.

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 sentence with no filler, and the most important fact ('first question') is front-loaded. It is appropriately concise, though the lack of a verb makes it slightly less informative than it could be.

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 start tool, the description is minimally adequate, but without annotations or an output schema it should clarify what the agent should expect when calling the tool and how the call relates to follow-up tools. These gaps leave room for mis-selection.

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 empty schema is fully complete. There are no parameter semantics for the description to add, and the baseline of 4 applies because there is nothing missing.

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 the resource ('Which Portuguese descent route? decision guide') and its position ('first question'), but it lacks an action verb. It does not explicitly say that invoking the tool starts the guide or displays the first question, so the tool's purpose is somewhat vague and must be inferred.

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 'first question' implies this tool should be used at the beginning of the decision guide, but the description gives no explicit guidance about when to use it versus sibling tools like checker_answer or checker_tree. No alternatives or exclusions are mentioned.

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 behavioral disclosure. It does reveal that the tool exposes the entire tree—every question, option, and verdict—rather than a partial view. It does not state the output format, size, or that it is a read-only informational tool, but for a zero-parameter static tree this is a tolerable 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, compact sentence that front-loads the key content ('Every question, option and verdict') and ends with the intended purpose. Every word adds value and there is 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 simple, parameterless tool with no output schema, the description is reasonably complete: an agent knows what the tool contains and why it would be used. It could be slightly stronger by describing the response shape or how the tree is organized, but nothing is critically missing for basic 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 and schema coverage is 100%, so there are no parameter semantics to clarify. The baseline for a parameterless tool is 4, and the description does not need to add anything.

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 resource: the complete decision tree for the 'Which Portuguese descent route?' guide, containing every question, option, and verdict. It does not use a direct imperative verb like 'returns' or 'provides', but the meaning is unambiguous and 'whole decision tree' distinguishes it from likely step-by-step 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?

'For reasoning end to end' gives an implied usage context: use this tool when you need the full tree rather than one step. However, it does not explicitly state when to prefer checker_start or checker_answer instead, nor does it name any alternatives or 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 provided, the description carries the full burden of behavioral disclosure. It transparently states that the tool initiates an enquiry, involves no purchase or payment, offers no guaranteed quote, is free, and explains what the response contains. This is strong disclosure for a zero-parameter descriptive 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 'Read first.' Every sentence adds value: it identifies the related tool, clarifies what is not happening, states it is free, and summarizes the return content. Nothing is wasted.

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 no input schema and no output schema, the description is remarkably complete. It explains the tool's role, the user's expectations, and the key elements of the response. The agent has enough information to decide when to call it and what it will receive.

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 description is not required to explain parameter behavior. The baseline of 4 applies, and the description appropriately focuses on the tool's purpose and output instead.

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 describes what submit_enquiry does rather than performing the enquiry itself. It explicitly says it returns who receives details, consent wording, and confirmation method, which fully clarifies the tool's scope and differentiates it from submit_enquiry.

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' directly instructs the agent to use this tool before submit_enquiry, giving clear contextual guidance. It also sets expectations by stating that nothing is bought, paid, or guaranteed, which helps prevent misuse. It does not explicitly name alternatives, but the intended sequence 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 provided, the description carries the burden of explaining behavior. It discloses the content and purpose of the tool well, making clear what the returned fields include. It does not explicitly state that the tool is read-only or describe the output format, but the context strongly implies a metadata retrieval operation.

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 sentences with no wasted words. The first sentence explains what the tool provides, and the second gives actionable guidance for using the output. It is appropriately concise and well-structured.

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 listing tool, the description is largely complete: it names the enquiry, lists the field attributes returned, and connects the output to submit_enquiry. It does not compare with enquiry_describe, but this omission is minor given the tool's simple and self-contained purpose.

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 description correctly focuses on what information the tool returns rather than parameter details. The mention of field keys and submit_enquiry is useful contextual guidance. The schema is empty, so there is no parameter ambiguity to resolve.

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 that the tool returns every field of the Eligible By Descent enquiry, including key, label, type, required status, help text, and options. The title reinforces that it represents the questions the enquiry asks. It is specific about the resource but does not explicitly distinguish itself from 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 description implies the tool should be used to discover field keys before calling submit_enquiry, stating 'Pass answers to submit_enquiry keyed by field key.' However, it does not explicitly compare against alternative tools like enquiry_describe or state when not to use this tool.

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 fully discloses the behavioral contract: no purchase/quote, consent is required, a confirmation token gates final submission, and the person receives an email with a click link before any provider sees the enquiry. It also exposes the exact consent wording the person must agree to.

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 dense but every sentence earns its place: purpose, non-use, step 1 behavior, step 2 behavior, and the email-link side effect are all covered. The two-step protocol is front-loaded and easy to follow.

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 complex two-step submit tool with no output schema and no annotations, the description covers necessary prerequisites, exact consent text, the summary/consent-line/confirmation output of step 1, and the post-submission email requirement. Nothing critical 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?

Although schema coverage is 100%, the description adds significant meaning: answers must be keyed by field key from enquiry_fields, consent must be true only after the person approves the specific consent line, and confirmation is the step-1 token to be reused in step 2. These semantics go well beyond the raw schema.

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 ('Submits an enquiry') and resource ('Eligible By Descent'), and immediately distinguishes the tool from a purchase or guaranteed quote. It also clearly frames the two-step nature of the action, so an agent can understand what the tool is for.

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 explicit when-to-use instructions: step 1 validates and returns a confirmation token, step 2 is used only after the person approves the summary. It also excludes non-uses ('NOT a purchase, NOT a guaranteed quote') and references enquiry_fields as the source of answer keys.

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