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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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It only states that this is the first question, without explaining what happens when the tool is invoked, whether it initializes any state, or what the returned content looks like.

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 redundant content. It is efficiently front-loaded with the key information, though it is somewhat under-specified in terms of 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 zero-parameter tool, the description is minimally adequate: it tells the agent this is the entry question of a decision guide. However, without an output schema or annotation, it lacks information about the expected response, session behavior, or how it connects to the rest of the guide.

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 does not need to compensate for missing parameter documentation. The empty schema with 100% coverage is sufficient, making parameter semantics a non-issue.

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 tool as the first question of the Cryptoasset financial promotion checker decision guide, which clearly conveys its role as the entry point. It does not use an explicit action verb, but the resource and stage are specific enough to distinguish it from the sibling tools at a basic level.

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 that this tool is used at the beginning of the decision guide, providing some usage context. However, it does not explicitly state when to use this tool versus alternatives like checker_answer or checker_tree.

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 behavioral burden. It discloses what content is included (every question, option, and verdict) and that it is intended for end-to-end reasoning, but it does not explicitly describe the call's return shape, size, or whether this is a pure read 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 a single, tightly worded sentence that front-loads the full scope ('Every question, option and verdict') and then states the intended reasoning use. Every phrase earns its place with no repetition or 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 parameterless retrieval tool with no annotations and no output schema, the description provides the essential context: what the tree contains and why an agent would use it. It could be more explicit about how the tree is returned, but for a zero-parameter whole-tree read, the description is close to sufficient.

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 zero parameters and 100% coverage, so the description has no parameter burden. No parameter documentation is needed, and the 0-parameter baseline of 4 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 clearly identifies the tool as containing the entire decision tree ('Every question, option and verdict') and names its domain (Cryptoasset financial promotion checker guide). It lacks an explicit verb like 'retrieve' or 'return,' but the title and phrasing make the resource unambiguous and distinguish it from stepwise siblings like checker_answer and checker_start.

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' implies the tool is meant for holistic reasoning over the full tree rather than single-step lookups. However, it does not explicitly state when to use this tool instead of checker_start, checker_answer, or the enquiry_* siblings, nor does it give any exclusion conditions.

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 clearly states that this tool returns an explanation of submit_enquiry, what the enquiry involves, what is not guaranteed, and what information is returned. This is more transparent than just relying on the tool name.

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 the most important instruction, 'Read first.' Every sentence adds meaningful information: what the tool does, what it does not do, and what it returns. There is 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 descriptive tool with no output schema, the description covers the essential context: what the tool explains, the relationship to submit_enquiry, the expected outcome, and what user-facing caveats to communicate. The agent can use this tool correctly with no further clarification needed.

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 explain. The description adds useful context about what the returned explanation covers, which aligns with the baseline for zero-parameter tools.

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 one that 'states plainly what submit_enquiry does,' giving a specific verb and resource. It also differentiates this tool from submit_enquiry and other siblings by explicitly positioning it as a read-first explanation rather than the action itself.

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 direct usage signal with 'Read first,' indicating this should be used before submit_enquiry. It also sets expectations about what an enquiry is and is not, which helps the agent decide when this tool is relevant. However, it does not explicitly contrast it with other siblings like checker_start or checker_answer.

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 it adequately conveys that this is an informational/read-only tool returning field metadata rather than performing an action. It also clarifies that answers belong in submit_enquiry, reducing the risk of misuse. It does not mention potential side effects, but none are implied or plausible for a field-listing 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, naming the resource and contents in the first sentence and immediately giving actionable next-step guidance in the second. Every sentence contributes meaningful information without repetition or 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?

Given that there are no parameters and no output schema, the description tells the agent what will be returned and how to connect it to the submission flow. It is not exhaustive about formatting or pagination, but for a simple metadata-listing tool the important context is present.

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 baseline for this dimension is 4. The description adds value by explaining how the returned field keys should be used when calling submit_enquiry, even though no parameter documentation is needed here.

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 returns every field of the FSMA Crypto Promotions Checker enquiry and enumerates exactly what each entry contains: key, label, type, required flag, help text, and allowed options. This distinguishes it from submission-focused siblings like submit_enquiry and gives an agent a precise idea of what to expect.

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 clear context by explaining that the output is meant to be used as a reference when passing answers to submit_enquiry, keyed by field key. It does not explicitly state when to prefer this over sibling tools such as enquiry_describe, but the purpose is clear enough to guide selection.

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 full disclosure burden and does so thoroughly: validation on the first call, return of a summary/consent line/token, the second-call submission, and an email link the person must click before any provider sees the enquiry. It also defines consent precisely.

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 long but every clause earns its place because the tool has a genuinely stateful two-step contract. It is front-loaded with the most important disambiguation ('NOT a purchase'), and the step-by-step structure is easy for an agent to parse.

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 no output schema and no annotations, the description adequately explains what the tool returns step by step, what the person receives, and the prerequisite consent behavior. It also points the agent to enquiry_fields for the answer keys, so nothing critical 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 already 100%, but the description adds essential meaning beyond the schema: it explains that answers are keyed by field key from enquiry_fields, that consent must be true, and that the confirmation token comes from step 1 and is used in step 2. This turns the parameters from a list into a workflow.

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 action and resource: 'Submits an enquiry to FSMA Crypto Promotions Checker', and immediately disambiguates with 'NOT a purchase, NOT a guaranteed quote.' It also names the two-step nature, which clearly separates it from siblings like enquiry_describe and enquiry_fields that only describe the form.

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?

Provides explicit, sequential instructions: call first with the answers and consent=true to obtain a token, then call again with the same answers plus the token only if the person agrees. It also states the consent condition and the email-link requirement, leaving no ambiguity about when and how to invoke the tool.

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