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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, the description carries the full burden of behavioral disclosure, but it only says this is the first question. It does not state whether calling it has side effects, what it returns, or how it relates to subsequent checker calls.

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 filler or extraneous detail. It is concise and immediately front-loads the tool's role, though it is somewhat thin on operational detail.

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 parameterless schema, the description is a minimal viable summary, but with no output schema and no annotations it should also indicate what the tool returns or what the next step is. An agent can call it, but may not know what to expect back.

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 description has no parameter semantics to add. The baseline for a parameterless tool is 4 here, and nothing is missing.

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 the first entry point of a named decision guide, which distinguishes it from later steps like checker_answer or checker_tree. It lacks an explicit action verb, but 'first question' conveys the tool's role.

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?

Calling this the 'first question' implies it should be used at the beginning of the decision guide, but there is no explicit guidance about when to use it versus siblings or what precedes or follows it. Usage is implied, not stated.

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 reveal that the tool exposes the complete set of questions, options, and verdicts, which is meaningful. However, it does not describe the return format, payload size, or any operational characteristics beyond content coverage.

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 written sentence that names the content and the intended use without filler. Every phrase adds information, and the purpose 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?

For a tool with no parameters, the description sufficiently explains what will be returned and why an agent would call it. The main gap is that the representation of the decision tree is not specified, and since there is no output schema, the agent must infer the shape from sibling conventions.

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 already reflects that with 100% coverage. The description adds no parameter detail, but none is needed; the baseline of 4 for parameterless 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 deliverable: every question, option, and verdict in the guide, for end-to-end reasoning. It is clear about what the tool returns, though it does not explicitly differentiate it from siblings like checker_start or checker_answer.

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 batch/full-tree use case, suggesting it is appropriate when the whole decision tree is needed rather than step-by-step traversal. However, it does not explicitly say when to prefer this over checker_start, checker_answer, or other siblings, 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?

With no annotations available, the description carries full responsibility for behavioral disclosure and does so well. It explicitly states that nothing is bought, ordered, or paid, that no quote is guaranteed, that it is free, and what information the tool returns, including recipient details, consent wording, and confirmation method.

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 'Read first' directive, then immediately states the core purpose. Every sentence adds relevant information: what the tool explains, what it does not involve, and what it returns.

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 explainer tool, the description is largely complete: it tells the agent what the tool explains and what output content to expect. It does not enumerate every possible output detail, but the described scope is sufficient for correct invocation and expectation-setting.

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 are no parameter semantics to elaborate. The schema fully covers this case, and the description appropriately focuses on the tool's behavior rather than input 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 explicitly states the tool's purpose: to plainly explain what submit_enquiry does. It adds crucial distinctions ('not a purchase, not a guaranteed quote') and clearly differentiates itself from the sibling submit_enquiry tool by being a descriptive/explainer tool 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 opening imperative 'Read first' signals that this tool should be used before engaging with submit_enquiry. It gives clear context about when the tool is appropriate, though it does not explicitly rule out sibling tools or phrase when-not-to-use 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?

There are no annotations, so the description carries the full burden of behavioral disclosure. It clearly implies a read-only, informational tool by describing fields and options, but it does not explicitly state side-effect-free behavior or what happens on error.

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 tightly-packed sentence that front-loads the core content and ends with a practical usage note. There is no wasted text or redundant restating 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?

For a parameterless tool with no output schema, the description adequately specifies what is returned and how to use the output. It could be slightly more explicit about the absence of parameters or side effects, but nothing essential 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?

The tool has zero parameters and 100% schema coverage, so the baseline for parameter semantics is 4. The description adds no parameter-level details because none exist, and its mention of field keys is relevant to output usage rather than input parameters.

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 what the tool provides: every field of the Farm Grant Deadlines enquiry, including key, label, type, required status, help text, and options. It lacks an explicit verb like 'retrieves' or 'lists', but the meaning is unambiguous and tied to a specific resource.

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 direct usage context by instructing that answers should be passed to submit_enquiry keyed by field key. It does not explicitly name alternatives or exclusions, but the intended workflow with sibling submit_enquiry 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 provided, the description fully carries the behavioral burden. It discloses the two-step validation/submission flow, that an email with a click link is sent and providers only see the enquiry after that link is clicked, and the exact consent phrasing. This is exemplary transparency for a mutation tool, covering side effects and prerequisites.

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 sentence is purposeful and actionable. It is structured as a clear step-by-step flow, front-loading the core purpose and the critical caveat ('NOT a purchase'). No redundant phrasing; all information contributes to correct invocation.

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?

Despite the complexity of a two-step confirmation process with a token and email verification, the description covers every essential aspect: step 1's return value (summary, consent line, token), step 2's requirement, and the email link behavior. There is no output schema, but the description explicitly describes the step 1 output, so no critical information 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?

The schema already documents all three parameters, but the description adds significant semantics: answers must be keyed by field keys from enquiry_fields, consent must match the exact consent line, and confirmation is the token returned from step 1. This goes beyond the schema's basic type descriptions and clarifies the relationships between parameters.

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 ('Submits an enquiry to Farm Grant Deadlines') and immediately clarifies it is NOT a purchase or guaranteed quote, which distinguishes it from potential confusion. It also details the two-step nature, making its purpose unambiguous and distinct from sibling tools like enquiry_describe or enquiry_fields.

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 explicit step-by-step usage: step 1 call with answers and consent, then step 2 only if the person agrees, with the confirmation token. It clearly specifies when to proceed and when not to. However, it does not explicitly name alternative tools for other tasks (e.g., enquiry_describe for describing fields), though it references enquiry_fields for keys, which partially covers context.

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