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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 behavioral disclosure. It indicates this is the first question in a decision guide, which suggests a read-like, non-mutating start, but it does not explain whether calling it advances state, displays the question, returns options, or requires any preceding action.

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, concise sentence with no filler. It front-loads the essential fact—that this is the first question of the guide—and 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 starting step, the description is enough to identify when to invoke it, but without an output schema or any note about what the tool returns (the question text? a prompt? a state change?), an agent cannot fully anticipate the result. It is minimally adequate but leaves the behavior of the call unclear.

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 coverage is effectively complete, so there is nothing the description needs to add about parameter meaning. The baseline of 4 applies because no parameter semantics are required.

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 resource—the first question of the 'What applies when you leave?' decision guide—and clearly positions it as the entry point. It is distinguishable from the sibling checker_answer and checker_tree tools, which appear to handle subsequent steps, though it does not state what the question itself is.

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 this tool should be used to begin the decision guide, but it never explicitly says 'call this when starting the guide' or contrasts it with checker_answer, checker_tree, or the enquiry_* tools. Usage context is present only by implication, not direct guidance.

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 behavioral burden itself. It discloses the output composition (every question, option, verdict) but does not explicitly say the tool 'returns' this data or describe any operational traits such as response size, structure, or read-only status. Still, the lack of parameters and the static description imply a safe, read-only retrieval, so it is adequate but not rich.

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 that front-loads the content ('Every question, option and verdict') and ends with the purpose. No filler; every word contributes.

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 parameters and no output schema, the description is largely self-sufficient: it tells an agent that the entire decision tree is available in one call for end-to-end reasoning. It doesn't detail the tree's format or how to combine it with sibling tools, but such details are not essential for a whole-tree resource.

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 takes zero parameters and the schema is an empty object, so there are no parameter semantics for the description to add. Baseline for 0-param tools is 4, and the description correctly avoids inventing parameter details.

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—the complete decision tree of the 'What applies when you leave?' guide—and signals its scope ('Every question, option and verdict', 'whole' from the title). It lacks an explicit verb like 'returns' or 'lists', which prevents a 5, but it clearly differentiates from stepwise siblings by emphasizing end-to-end content.

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 'for reasoning end to end' explicitly states the intended use case: obtaining the full tree rather than a single answer or start step. It does not name the sibling tools or state when not to use it, so it falls short of a 5, 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 full burden. It discloses what the tool returns: an explanation of submit_enquiry, recipient details, consent wording, and confirmation method. However, it does not explicitly state that this tool itself has no side effects or how the output is structured.

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 brief and front-loaded with 'Read first,' making the intended use immediately clear. It is slightly repetitive in saying 'States plainly what submit_enquiry does' and then restating that content, but overall it is concise and well organized.

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 informational tool, the description covers what the tool explains, the key caveats about the enquiry not being a purchase or guaranteed quote, and the specific content it returns. Without an output schema, a bit more detail on the exact output format would be helpful, but this is sufficient for the tool's simplicity.

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 input schema is empty, so there are no parameter semantics to clarify. The baseline of 4 applies because the description adds useful context about the tool's informational purpose even though no parameter documentation is 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 clearly states that the tool explains what submit_enquiry does: it starts an enquiry with human providers who quote directly, with no purchase or guarantee. It is distinct from the sibling submit_enquiry tool, though it does not explicitly contrast itself with the other 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?

The instruction 'Read first' clearly signals when to use this tool: before using submit_enquiry. It provides context for a pre-submission explanatory step, but it does not explicitly mention alternatives or when not to use 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. It transparently enumerates what the tool exposes and clarifies that submission happens elsewhere via submit_enquiry, implying this is a non-mutating metadata operation. It could be stronger with an explicit 'read-only' statement, but the behavior is otherwise well disclosed.

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 output contents, followed by a useful cross-reference to submit_enquiry. Every phrase earns its place; there is no filler or repetition of the title.

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 must explain return values, and it does so thoroughly: key, label, type, required, help text, and allowed options. It also names the downstream consumer (submit_enquiry) and how to key answers. Nothing else is needed for an agent to correctly use this metadata 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 and an empty input schema, so there are no parameter semantics to document. The baseline of 4 applies; the description correctly avoids inventing parameters and instead directs relevant field-key information toward submit_enquiry.

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 specifies the resource (the Exit Tax Estimator enquiry) and the exact content of the tool's output: field key, label, type, required flag, help text, and allowed options. It is identifiable as a field-metadata listing tool, though it lacks an explicit verb like 'returns' or 'lists' and does not directly contrast itself with the 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 gives a practical usage hint: 'Pass answers to submit_enquiry keyed by field key,' which tells the agent how to consume the output. However, it does not explicitly state when to prefer this tool over related siblings such as enquiry_describe or checker_tree, so the usage guidance is implied rather than explicit.

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 states this is not a purchase, that step 1 only validates and returns a summary/consent line/token, that step 2 actually submits, and that the person must click an emailed link before any provider sees their details. It also spells out the full scope of consent, which is exactly the kind of behavioral context an agent needs here.

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?

It is long but dense and well front-loaded, opening with the critical not-a-purchase disambiguation before the step-by-step instructions. The main inefficiency is that the full consent quotation largely duplicates the consent parameter description already in the schema.

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 two-step, three-parameter, nested-object tool with no annotations and no output schema, the description covers the workflow, the gate conditions, the side effects (email, provider visibility) and the meaning of the returned artifacts. Nothing an agent needs to invoke it correctly appears to be 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 real meaning: answers are keyed by the field key from enquiry_fields, consent must be true at both steps, and confirmation is the token returned by step 1 after the person approves. That sequencing semantics goes beyond the schema's per-field wording.

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 title and description state a specific verb and resource (submit an enquiry to human providers) and explicitly disambiguate it from a purchase or guaranteed quote, which is the primary risk of misreading this tool. It also names the sibling enquiry_fields as the source of field keys, so an agent can place it relative to its siblings without opening their schemas.

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 lays out the exact two-step flow: step 1 with answers and consent=true, step 2 only after the person agrees, with the same answers plus the confirmation token. The condition that selects step 2 is stated explicitly rather than left to inference, and the dependency on enquiry_fields is named.

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