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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. It does disclose the key behavior—returning either the next question or a final verdict—but it does not mention whether the flow state is mutated, what happens for invalid choices, or any session/state requirements.

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?

A single, information-dense sentence. The title and description work together without repetition or filler, and the key input/output behavior is front-loaded.

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?

Adequate for a two-parameter tool: it names both inputs and describes the possible return. However, with no output schema and no annotations, it falls short of fully describing the response shape, error behavior, or what the caller should do with the returned next question/final verdict.

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 has 0% description coverage, so the description's clarification that `question` is a question id and `choice` is the chosen option's index is essential. It adds meaning beyond the raw property names and types, though it omits details like how to obtain valid question ids or bounds beyond minimum 0.

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 operation: given a question id and a choice index, return the next question or final verdict. This clearly differentiates it from sibling tools like checker_start or checker_tree, which have different roles in the flow.

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?

Usage is implied: use this when a user has answered a question and the flow should advance. However, it does not explicitly state when not to use it, what prerequisites exist (e.g., starting with checker_start), or how it relates to alternatives.

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 carries the full load and does so well: it discloses the validation step, the returned summary/consent line/token, the double-confirmation requirement, and crucially that the inquirer must click an emailed link before any provider sees the enquiry. It also spells out the full consent scope (up to three advisers, notification per firm, opt-out). This is unusually rich behavioral disclosure.

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 non-purchase disclaimer and sequenced as Step 1/Step 2, which is easy to follow. It is long and the embedded verbatim consent text is dense, though that text is arguably load-bearing for correct consent handling.

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 mutation-style two-phase flow with no output schema and 100% schema coverage, the description supplies everything an agent needs: sequencing, returned values at each step, the consent precondition, and the downstream email-confirmation gate. Nothing material 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: answers are keyed by field key from enquiry_fields, consent=true is defined against the quoted consent text, and confirmation is described as the token from step 1 after approval. It clarifies inter-call state that the schema alone does not.

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 opens with a precise verb+resource ('Submits an enquiry to Eligible By Descent') and immediately draws the boundary that matters most: 'NOT a purchase, NOT a guaranteed quote.' This clearly separates it from the read-only siblings (enquiry_fields, enquiry_describe) and checker 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?

It gives a concrete two-step call protocol — step 1 with answers+consent, step 2 only 'if the person agrees' with the token — which tells the agent exactly when each call is appropriate. It does not name the sibling tools that supply inputs (enquiry_fields, enquiry_describe), so routing among alternatives is only implied.

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