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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 must carry the full burden of behavioral disclosure. It only says the tool is 'the first question' of a guide, without explaining what happens when it is called, what it returns, whether it asks a question, or how it relates to subsequent steps. This is a minimal hint rather than transparent behavior.

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 wasted words and the key 'first question' idea is front-loaded. It is concise, but it is also so minimal that it borders on under-specification, so it does not earn a higher score.

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 and no annotations, the description should clarify what invoking 'start' actually does and how it fits into the decision guide flow. It only states that it is the first question, leaving the agent without enough context to know what result to expect or how to proceed to checker_answer or checker_tree.

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 input schema is empty and the tool has zero parameters, so there is nothing for the description to document. The baseline for 0-parameter tools is 4, and no parameter information 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 identifies the tool as the first question in the 'Which lever applies?' decision guide, which gives some sense of its role as a starting point. However, it is a noun phrase rather than a clear verb+resource statement, and it largely restates the title, so the tool's actual action remains vague.

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?

No explicit guidance is given about when to use this tool versus siblings like checker_answer or checker_tree. The phrase 'first question' implies it comes at the beginning, but there is no clear statement about prerequisites, exclusions, 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?

With no annotations, the description carries the burden of disclosure. It states the tool's output scope ('every question, option and verdict') and intended purpose, making the read-only retrieval behavior clear. It does not describe the exact output format, but for a zero-parameter static resource this is a minor 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?

A single, dense sentence that front-loads the core content ('every question, option and verdict') and states the intended reasoning use. No wasted words.

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 with no output schema, the description adequately conveys what the tool returns and why it would be used. It could be more explicit about how the tree is structured or that it returns the complete guide in one response, but overall it is sufficient for an agent to decide to call 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 input schema has zero parameters, so the baseline is 4. The description adds no parameter-specific details, but none are needed since the tool accepts no input.

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 provides every question, option, and verdict from the 'Which lever applies?' guide, which identifies the resource and content. It is understandable and distinct from siblings like checker_answer or checker_start, though it does not explicitly name them.

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 a use case: when an agent needs the full decision tree rather than a single step or verdict. However, the description offers no explicit guidance on when to choose this tool over sibling tools like checker_answer or checker_start.

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 disclosure burden. It does this well by explicitly stating that nothing is bought, ordered, or paid, that no quote is guaranteed, and that the tool is free. It also discloses what the tool returns: who receives details, consent wording, and how the person confirms.

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 short and front-loaded with 'Read first,' then delivers the essential behavioral facts in two more sentences. Minor redundancy with the title and the unexplained 'STR Rules Check' abbreviation keep it from being a perfect 5.

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, the description covers what the tool does, what effects it does not have, and what information it returns. It is complete enough for its likely role as an explainer before submit_enquiry, though 'STR Rules Check' remains undefined and no output format is described.

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 input schema has zero parameters, so there are no parameter semantics for the description to explain. The description appropriately focuses on behavior and return value rather than inputs, which aligns with the baseline for no-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 states that the tool 'States plainly what submit_enquiry does on STR Rules Check' and lists the key behaviors and returns. It references a specific sibling tool, submit_enquiry, which helps distinguish it from the checker and enquiry sibling tools. The phrase 'STR Rules Check' is unexplained, which slightly reduces clarity.

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 directive 'Read first' strongly implies that this tool should be consulted before using submit_enquiry. However, it never explicitly states when to use this tool versus alternatives like enquiry_fields or submit_enquiry itself, and it provides no exclusion criteria.

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 usefully implies this is a read-only metadata retrieval operation and distinguishes it from submit_enquiry by directing answers there. It does not disclose authentication requirements, error behavior, or response format, but for a zero-parameter metadata tool the implied behavior is reasonably clear.

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 tight sentences with no filler. The first sentence enumerates the tool's output content, and the second connects it to the sibling tool that consumes those keys. Every sentence 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?

With no output schema and no annotations, the description adequately covers what the agent receives by naming the field attributes and the key relationship to submit_enquiry. It is complete enough for a simple, zero-parameter retrieval tool, though it does not describe response formatting or edge cases.

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 no parameters, so the baseline of 4 applies. The description adds relevant conceptual meaning by explaining that the returned field keys should be used as answer keys in submit_enquiry, which is more useful than the empty schema alone.

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 fields of the STR Rules Check enquiry — and enumerates exactly what is included: key, label, type, required flag, help text, and allowed options. It lacks an explicit verb such as 'lists' or 'returns', but the meaning is unambiguous and it is distinct from the submit_enquiry 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 description gives workflow context by saying 'Pass answers to submit_enquiry keyed by field key,' which implies this tool is used to discover the available fields before submitting. However, it does not explicitly say when to prefer this tool over related siblings like enquiry_describe or checker_tree, nor does it state any 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?

No annotations, so the description carries the full burden and does well: it discloses the two-step flow, the required confirmation token, the consent semantics, and the crucial behavioral detail that the person must click an emailed link before any provider sees the enquiry. It does not describe validation-failure behavior or what happens if the token is reused/omitted, which keeps it from a 5.

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 key disambiguation and then a clear step-by-step structure. It is somewhat long, and the consent text is quoted verbatim in both the description and the consent parameter schema, which is redundant, but the prose otherwise 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?

With no output schema, the description must cover returns — and it does, naming the summary, the consent line and the confirmation token from step 1, plus the downstream email/link behavior. For a complex multi-step, nested-parameter tool, nothing an agent needs to call it correctly 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 real meaning: 'answers' are keyed by field key from enquiry_fields, and 'confirmation' is the token returned by step 1 — the schema alone does not link these parameters to their source. This contextual linkage lifts it above baseline.

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 verb+resource ('Submits an enquiry to STR Rules Check') and immediately disambiguates the outcome with 'NOT a purchase, NOT a guaranteed quote.' An agent can distinguish it from the checker_* siblings and from enquiry_fields/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?

Gives an explicit two-step protocol: step 1 with answers and consent=true, then step 2 only if the person agrees, passing the confirmation token. The conditions that gate each call and the reason for the second call are stated directly.

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