site

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?

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?

No annotations are provided, so the description carries the full burden and does so: it discloses validation on step 1, the returned summary/consent line/token, the mandatory email-link confirmation before any provider sees the data, and the exact consent text. It also discloses downstream effects (up to three consultants contacted, opt-out available).

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 critical 'not a purchase' framing, then organized as Step 1 / Step 2, which is efficient for a multi-call protocol. It is long, and the full consent sentence is duplicated in the schema's consent description, costing some redundancy.

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 compensates by describing what each step returns and what happens after submission. For a nested-object, two-phase 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 only supplied on the step-2 call. That sequencing constraint 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 (submit an enquiry to Venue Capacity Check) and immediately bounds scope with 'NOT a purchase, NOT a guaranteed quote,' separating it from siblings like enquiry_fields and enquiry_describe. An agent can identify this as the terminal submission step without opening any 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 conditional routing: Step 1 with answers + consent=true, then Step 2 only 'if the person agrees,' with the confirmation token. The gating condition for the second call and the required human approval are spelled out rather than inferred.

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