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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 only states what the tool represents, not what happens when it is called, whether it starts a session, what data it returns, or whether any state changes occur.

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 short sentence with no redundancy and the core identity is front-loaded. It is concise, though the brevity comes at the expense of behavioral detail captured elsewhere.

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 an entry-point tool with no output schema and no annotations, the description should explain what the caller will receive and what to do next. It provides neither the return shape nor any connection to checker_answer, so an agent cannot fully understand the workflow from the description alone.

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 no parameter meaning for the description to clarify. This is the appropriate baseline for a no-parameter tool.

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 that the tool is 'The first question of the CQC registration checker decision guide', which identifies its role as the entry point. However, it lacks an action verb like 'starts' or 'displays', and it does not explicitly distinguish itself from siblings such as checker_answer or checker_tree beyond the positional clue in the name.

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?

There is no guidance about when to call this tool versus checker_answer, checker_tree, or the enquiry_* tools. The name 'Start' implies it should be called first, but the description never states this or mentions alternatives, leaving usage entirely 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It explains the content scope but not how the tree is returned, whether it is read-only, how large the payload is, or how the agent should traverse it. There is no mention of response format or 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, front-loaded sentence that wastes no words. It immediately conveys the complete scope of the tool ('Every question, option and verdict') and ends with its intended purpose.

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 parameterless tool, the description adequately states the tool's content scope and purpose. However, it lacks any indication of the output structure or how to consume the decision tree, and without an output schema or annotations, an agent may still be uncertain about what kind of response to expect.

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 zero parameters and the schema has no properties, so there is no parameter information that the description must supplement. The description compensates by clarifying what the tool actually delivers, which is sufficient for a parameterless tool.

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 containing the complete decision tree for the CQC registration checker, enumerating its contents as 'every question, option and verdict.' This distinguishes it from the sibling tools like checker_answer and checker_start, though it lacks an explicit operative verb like 'retrieve' or 'return.'

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' implies that this tool is intended for whole-tree reasoning rather than stepwise interactions. However, it does not explicitly state when to prefer checker_tree over checker_answer or checker_start, nor does it mention any exclusions or limitations.

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 burden. It discloses the tool's read-only character through 'States' and 'returns', and details what a caller will learn: recipients, consent wording, and confirmation method. It does not explicitly rule out side effects, but the language strongly implies an informational 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 two short sentences with the imperative 'Read first' front-loaded. Every clause adds useful information: what the tool does, what it is not, and what it returns. No filler.

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 helper, the description is complete: it states the tool's purpose, when to use it, and the contents of its return value. An agent can invoke it without additional context.

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 vacuous at 100%. There is nothing for the description to explain about inputs, so the baseline 4 for no-parameter tools applies.

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 action—'States plainly what submit_enquiry does'—and gives concrete scope: an enquiry with human providers, no purchase, no guaranteed quote, free. It clearly separates this meta/description tool from the actual submit_enquiry sibling, though it does not explicitly name that sibling.

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 opening 'Read first' is a direct usage signal telling the agent to consult this tool before proceeding with an enquiry. It explains what the tool returns, so an agent can decide it is the right overview step, but it does not give explicit when-not-to-use or alternative-routing 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 provided, the description carries the full burden of behavioral disclosure. It discloses what content the tool exposes and how the result relates to submit_enquiry, which is valuable context. It does not explicitly state that the operation is read-only or describe the exact response shape, but for a parameterless metadata-listing tool these are minor gaps.

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?

Two dense, front-loaded sentences with no filler. The field attribute list is compact, and the cross-reference to submit_enquiry is a single useful clause that 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 no-argument, no-annotation metadata tool, the description gives a complete inventory of returned attributes and tells the agent how to use the result. The exact response structure is not specified, but the explicit field enumeration makes the tool usable without it.

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 self-explanatory and the baseline of 4 applies. The description adds contextual meaning around the 'key' concept by explaining its role in submit_enquiry, which reinforces rather than repeats the 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 clearly identifies the tool as providing a list of every field in the CQC Registration Checker enquiry and enumerates the key attributes returned (key, label, type, required, help text, options). It is specific about the resource and content, though it lacks an explicit verb like 'returns' or 'lists' and does not directly distinguish itself from sibling tools such as 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 clear practical guidance: answers should be passed to submit_enquiry keyed by field key, which tells an agent why and when to use this tool. It does not explicitly mention when not to use it or compare it with alternatives, but the connection to submit_enquiry is a strong usage signal.

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 behavioral burden, and it does so thoroughly. It discloses the two-step validation flow, the returned summary/consent line/token, the need to show the person before proceeding, the email with a link, and the fact that providers only see the enquiry after the link is clicked. It also quotes the exact consent wording.

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 element is necessary. It front-loads the key caveat ('NOT a purchase'), then structures the behavior into clear Step 1 and Step 2 stages. The consent text is quoted exactly, adding precision without 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?

Given the tool's complexity — a two-step human submission flow with no output schema — the description covers validation, return values, user consent, confirmation token reuse, email delivery, and the click-before-view condition. Nothing essential for correctly invoking the tool 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?

Although schema coverage is 100%, the description adds meaningful workflow semantics beyond the schema: answers must be keyed by field keys from enquiry_fields, consent must be true and reflect the quoted consent line, and the confirmation token comes from step 1. These details connect the parameters to the two-step process rather than merely restating their types.

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 verb ('Submits an enquiry') and the resource ('to CQC Registration Checker'), while also explicitly contrasting it with a purchase and a quote. It effectively distinguishes this submission action from the sibling exploration and field-description tools by emphasizing the two-step submission process.

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 provides detailed step-by-step guidance: step 1 calls with answers and consent, then step 2 requires the consenting user and confirmation token. It does not explicitly name sibling alternatives, but the 'NOT a purchase, NOT a guaranteed quote' clarifications and the two-step workflow give sufficient when-to-use context.

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