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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 full responsibility for disclosing behavior. It only states what the tool is ('the first question') rather than what happens when invoked, such as whether it returns a question, starts a session, or has 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, compact sentence that conveys the tool's position in the decision guide without redundant phrasing or filler. It is appropriately sized for a tool with no parameters and simple semantics.

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 tool, the description is minimally adequate, but it leaves ambiguity about what the agent will receive after calling and how this connects to checker_answer or checker_tree. A brief mention of the return value or next step would make it complete.

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 100% schema description coverage, so there is nothing for the description to clarify. Per the baseline for zero-parameter tools, this dimension is well satisfied.

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 as 'Food temperature record checker decision guide' and positions this tool as its 'first question,' which is more informative than a tautology. However, it lacks an explicit action verb such as 'returns' or 'starts,' so the agent must infer what calling it actually does.

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 word 'first' implies this tool is the entry point to the decision guide, giving the agent a weak usage signal. It does not explicitly compare against sibling tools like checker_answer or checker_tree, nor state 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 provided, the description carries the full burden of behavioral disclosure. It does disclose content coverage and that the tool represents the whole guide tree, but it does not describe return format, traversal mechanics, or any side effects. There is no contradiction, but the behavioral detail is thinner than it could be.

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 with no filler. The key information—complete coverage of questions, options, and verdicts—is front-loaded, and every phrase contributes meaning beyond 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?

For a zero-parameter tool with no output schema and no annotations, the description gives a reasonable sense of what is included but not exactly what the agent will receive or how this tree relates to the sibling checker tools. It is minimally viable but leaves room for more explicit guidance.

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 no parameters, so there is no parameter information for the description to add. The description therefore cannot be penalized for missing parameter guidance, and the zero-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 resource as the complete decision tree for the Food temperature record checker and enumerates its contents ('every question, option and verdict'). It distinguishes itself from stepwise siblings like checker_answer and checker_start by emphasizing end-to-end coverage, though it lacks an explicit verb like 'returns'.

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 the tool should be used when the agent needs the full decision tree rather than a single step. However, it does not explicitly state when not to use it or name alternatives such as checker_start or checker_answer, leaving routing mostly to inference from sibling names.

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 states the tool 'returns who receives the details, the consent wording, and how the person confirms' and clarifies what it does not do ('Nothing is bought, ordered or paid; no quote is guaranteed'). It reveals the output content and the non-transactional nature of the enquiry. It does not explicitly declare the tool itself side-effect-free, but the 'states/returns' framing makes its read-only nature reasonably evident.

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, front-loaded with the imperative 'Read first,' and each clause adds a distinct point: target tool, non-purchase behavior, and return content. There is minor redundancy with the title, but no wasted sentences.

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 zero parameters, no annotations, and no output schema, the description provides enough orientation: what the tool is for, its target, and its returns. It mentions the consent wording and confirmation flow, which are actionable for deciding whether to call submit_enquiry. It could name alternative/successor tools, but that is not essential for calling this simple describe 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 input schema has zero parameters, so the schema itself is already complete; the baseline for a no-parameter tool is 4. The description reinforces the informational nature but has no parameter details to add.

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 uses a concrete verb ('states plainly') and names the subject ('what submit_enquiry does on Temperature Log'), so an agent understands this is an informational tool about a specific sibling. The title also clarifies the outcome is 'not a purchase, not a guaranteed quote,' distinguishing it from action-oriented tools like submit_enquiry. This clearly separates it from the 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 opening 'Read first' explicitly tells an agent to consult this tool before acting, and the body explains it is the plain-language guide to submit_enquiry's behavior. It does not explicitly discuss alternatives or exclusions, so it stops one step short of full 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, the description carries the burden of explaining behavior. It does disclose the exact kind of data returned and the expected downstream usage, which is helpful. However, it never explicitly states that this is a read-only retrieval operation, nor does it mention potential errors or limitations, leaving some behavioral ambiguity.

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 core purpose, then lists the contained data attributes, and ends with a practical usage pointer. Every clause earns its place with no redundancy 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 no-input, no-output-schema metadata tool, the description is complete. It explains what data the tool returns, enumerates the fields, and clearly connects it to submit_enquiry, which is the main action an agent would need to take with that data.

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 full schema description coverage, so there are no parameter semantics for the description to add. The baseline for zero parameters is 4, and the description appropriately focuses on the output and how it should be used rather than inventing parameter guidance.

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 exposes every field of the Temperature Log enquiry, including key, label, type, required status, help text, and allowed options. The purpose is specific and the resource is named, though it does not explicitly differentiate itself from the sibling enquiry_describe tool.

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 actionable usage context: 'Pass answers to submit_enquiry keyed by field key.' This tells the agent why the tool matters and how the returned field keys should be used. It does not explicitly contrast this tool with alternatives, but the usage direction 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 and does so: it discloses validation behavior, the returned summary/consent line/confirmation token, the double-opt-in email link that must be clicked before any provider sees the enquiry, and that contact details go to up to three named suppliers. Side effects and consent semantics are fully surfaced.

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 purpose and the not-a-purchase caveat, then structured as Step 1/Step 2. The verbatim consent paragraph is lengthy and partially duplicates the consent parameter's schema description, costing some efficiency, but it is legally load-bearing rather than 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 multi-step, side-effect-heavy tool with nested answers, no output schema and no annotations, the description supplies everything needed: the two-call protocol, what each call returns, consent wording, and the downstream email verification. Nothing an agent needs to invoke 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, 'consent' must be true only after the person agreed, and 'confirmation' is the step-1 token passed on the second call. That sequencing context is not derivable from the 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?

States a specific verb+resource ('Submits an enquiry to Temperature Log') and immediately disambiguates what it is not ('NOT a purchase, NOT a guaranteed quote'). It also names the related sibling concept (field keys from enquiry_fields), so an agent can place it against enquiry_describe/enquiry_fields without opening a schema.

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 explicit sequential conditions: Step 1 call with answers+consent=true, Step 2 only if the person agrees, passing the confirmation token. This is strong when-to-use guidance for the two-phase flow, though it does not contrast against the other enquiry_* siblings for adjacent informational tasks.

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