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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 states the operation returns the next step, but does not disclose what happens on invalid inputs, whether any state is mutated, or the nature of a final verdict. For a decision-taking tool, such context is important.

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 core interaction. No wasted words, though it could have added a bit more detail without becoming verbose.

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 2-parameter tool with no output schema and no annotations, the description gives the barest interaction model. It lacks details on edge cases, response format, usage context, and relationship to sibling tools, making it insufficient for robust autonomous invocation.

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 0%, so the description must compensate. It mentions the question id and the choice index, but does not explain what the 'choice' index refers to (e.g., zero-based vs. one-based), or the expected format of 'question'. The schema only specifies types, so the meaning is insufficiently defined.

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 specific verb ('return'), resource (next question or final verdict), and states the input (question id and chosen option index). It clearly indicates the tool's function, though it does not explicitly differentiate it from sibling tools in the description 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 implicit usage context: it should be used when you have a question id and a chosen option. However, it does not provide explicit guidance on when to use this tool versus alternatives like checker_tree, or what to do if the question is invalid or the choice is out of range.

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 to communicate safety or side effects, so the description carries the full burden. It only says the tool 'is' the first question, not what calling it does, what it returns, or whether it mutates any state. For a tool with zero annotations, this is too thin to be considered behaviorally transparent.

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 or repetition. It front-loads the essential positional information about the tool and is appropriately sized for a tool with no parameters.

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?

There is no output schema and no annotation coverage, so the description should clarify what the agent can expect from calling the tool. The phrase 'first question' gives a reasonable clue, but the description does not specify the return shape, whether it just displays the question, or how the response feeds into checker_answer. It is minimally viable but leaves important context implicit.

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 explain any inputs. The baseline for a parameterless tool is high, and there is nothing missing here from a parameter semantics perspective.

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 Venue capacity checker decision guide, which gives some positional context. However, it lacks a concrete action verb such as 'returns' or 'starts', so it is more of a label than a functional description. It does not explicitly distinguish itself from sibling tools like checker_answer beyond the word 'first'.

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 timing is implied: because it is described as the 'first question', an agent can infer it should be called before checker_answer or checker_tree. There is no explicit guidance about when not to use it or which alternative to choose, so the guidance is present only by implication.

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 contains (the tree) and not what it does when invoked (e.g., returns the full tree, is read-only, or has no side effects). This leaves the agent to guess the tool's runtime 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 one short sentence with no filler words. It could be slightly improved by starting with a verb to make it a full sentence, but it is efficient and readable.

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, annotation-free tool with no output schema, the description conveys the core idea (the full guide tree for end-to-end reasoning) but does not say what the agent will receive back or how to choose it over siblings. Slightly more guidance 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 the input schema is trivially 100% covered, so the description has no parameter documentation burden. Baseline for 0 params is 4, and the description does not interfere with parameter understanding.

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 precisely: the whole decision tree of the Venue capacity checker guide, and enumerates its contents ('every question, option and verdict'). This helps distinguish it from likely stepwise siblings like checker_answer or checker_start, though the lack of an explicit verb (e.g., 'returns', 'provides') stops it from being a 5.

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 intended use case—consulting the full tree rather than a single node—but there is no explicit contrast with alternatives or exclusions. The agent must infer when this is preferred over sibling tools.

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 well. It states that nothing is bought, ordered, or paid, that no quote is guaranteed, that it is free, and that it returns specific information including recipient details, consent wording, and confirmation method. This gives the agent a clear behavioral and output contract.

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 three sentences, front-loaded with the key instruction 'Read first,' and every sentence adds essential information. There is no repetition of schema content or filler, making it efficient and easy 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 there are no parameters, no annotations, and no output schema, the description sufficiently covers what the tool does, what it returns, and how it relates to submit_enquiry. An agent has everything needed to invoke it correctly and understand the result.

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 is empty, so parameter semantics are not needed. With 0 params, the baseline is 4, and the description appropriately focuses on the tool's purpose and output instead of parameter 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 uses a specific verb ('states') and resource ('what submit_enquiry does'), and clearly distinguishes this tool from submit_enquiry by presenting it as the read-first overview. The title reinforces the non-purchase, non-guaranteed nature, making the tool's purpose unmistakable.

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?

'Read first' explicitly signals when to use the tool: before submit_enquiry. It also clarifies the expected outcome and the fact that no transaction occurs, which helps an agent decide whether this is the right pre-submission step. It does not explicitly name alternatives, but the sibling context makes the intended placement 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?

No annotations are provided, so the description carries the full behavioral disclosure burden. It clearly enumerates the returned content but does not state whether this is a read-only metadata lookup, how results are structured, or whether any side effects exist. The submit_enquiry pointer is helpful but relates to another tool's 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, well-structured sentence that front-loads the resource name, lists the exact data attributes, and ends with a practical cross-reference to submit_enquiry. Every clause earns its place with 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?

As a simple, parameterless metadata tool with no output schema and no annotations, the description provides the essential return-value information: field key, label, type, required status, help text, and allowed options. It also gives one integration hint. It does not specify the response format (e.g., array or object), but for this simplicity that is a minor gap.

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 useful semantic context by explaining that returned field keys are the same keys used when submitting answers to submit_enquiry, which helps an agent connect this tool's output to a sibling tool's 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 identifies the resource: every field of the Venue Capacity Check enquiry, and enumerates what is included (key, label, type, required, help text, options). It does not use an explicit verb like 'list' or 'retrieve', and the sibling 'enquiry_describe' could overlap in purpose, so it falls short of a 5.

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 implies that this tool is useful before submitting answers by saying 'Pass answers to submit_enquiry keyed by field key.' However, it does not explicitly state when to use this tool versus alternatives such as enquiry_describe or the checker_* tools, and gives no 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 provided, the description carries the full burden and does so thoroughly. It discloses that this is not a purchase or quote, that validation happens in step 1, that a confirmation token is required for step 2, and that the person receives an email link that must be clicked before any provider sees the enquiry.

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 sentence contributes essential information: what the tool is not, the two-step flow, the consent requirement, and the email-click behavior. It front-loads the core purpose and then provides the necessary procedural 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?

Despite having no output schema, the description explains what step 1 returns (summary, consent line, confirmation token) and what happens after step 2 (email with a click link before provider visibility). For a multi-step tool with consent and confirmation mechanics, this is complete enough for correct invocation.

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 crucial meaning beyond the schema: answers must be keyed by fields from enquiry_fields, consent must match the exact consent text, and confirmation is the token returned from step 1. This makes the parameters actionable rather than merely typed.

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 verb and resource ('Submits an enquiry to Venue Capacity Check') and clearly distinguishes itself from a purchase or a guaranteed quote. It also positions the tool as a two-step submission flow, making its purpose clear relative to the sibling enquiry 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 description provides explicit step-by-step usage instructions: first call with answers and consent=true to get a summary and token, then call again with the token only after the person agrees. It also states the condition that consent requires the person to have read and agreed to the exact consent line.

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