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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 of behavioral disclosure. It explains the input-output relationship but does not disclose error behavior, side effects, or the branching condition between 'next question' and 'final verdict'. This leaves the agent guessing about important 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?

A single sentence with no filler. The conditional input is front-loaded and the output is stated directly, making it easy to parse quickly.

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?

Although the tool has only two simple parameters, it appears to be part of a multi-step flow with siblings like checker_start and checker_tree. The description does not clarify how to obtain a valid question id or when a 'final verdict' is returned, and there is no output schema to compensate.

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 description coverage is 0%, so the description must compensate. It adds useful meaning by clarifying that 'question' is an id and 'choice' is the chosen option's index, but it omits details about id format or how choices are enumerated.

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 clear action: given a question id and a choice index, it returns the next question or final verdict. This makes the tool's core purpose understandable, though it does not explicitly distinguish it from siblings like checker_start or checker_tree.

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 'Given a question id and the chosen option' implies when the tool should be used, but it does not explain prerequisites, such as obtaining the question id from checker_start, nor does it specify when to prefer this over sibling tools. Usage context is only implicit.

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 does not describe what the tool returns, whether it initiates a flow, asks a question, or changes state—leaving the agent with no behavioral information beyond the title.

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 concise sentence with no waste, but it is under-specified rather than efficiently informative. It is short without adding meaningful behavioral or usage 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 no annotations, no output schema, and only a positional description, an agent cannot determine what invoking this tool will do, what to expect in response, or how to proceed afterward. Low parameter complexity reduces the burden, but the behavioral vacuum remains significant.

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 has 100% coverage with an empty properties object, so there is no parameter ambiguity for the description to resolve. Baseline 4 applies for parameterless 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 identifies this as the first question of a decision guide, which conveys a clear entry-point role. However, it is a noun phrase rather than a verb+resource statement, and it does not say what actually happens when invoked beyond that positional 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?

The phrase 'first question' implies that this tool starts the guide, but there is no explicit statement of when to use it versus checker_answer or enquiry_* siblings. No conditions, exclusions, or alternative routing are provided.

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?

Annotations are absent, so the description carries the full burden of behavioral disclosure. It only lists the content contained (questions, options, verdicts) but does not state whether this is a static read-only dump, whether it has side effects, or what the agent should do with the returned information. The lack of any safety or mutation hints is a notable 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?

The description is very short and front-loads the key content coverage. The phrase 'Check an agent in four questions guide' is slightly awkward, but the sentence is otherwise compact and contains 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?

For a zero-parameter, static-content tool, the description gives a reasonable idea of the payload, but it does not explain how this tree relates to the sibling tools or when an agent should consult it. With no output schema and no annotations, a bit more context (e.g., 'use this to understand all branches before invoking checker_start') 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, so the baseline is 4. There is nothing for the description to add about parameters; the description correctly focuses on content rather than input semantics.

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 the tool provides every question, option, and verdict of the decision guide, which clearly identifies the resource and scope. It is distinguishable from siblings like checker_answer and checker_start by being the complete tree, though it lacks an explicit verb like 'retrieve' or 'return'.

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 when-to-use guidance is provided. The description does not mention when an agent should call this tool instead of siblings such as checker_answer or checker_start, nor does it specify whether it is meant for initial orientation, debugging, or end-to-end traceability.

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 safety burden. It clearly frames the tool as descriptive/read-only ('States', 'returns') rather than performing the enquiry, and it surfaces key facts about the enquiry (no purchase, no guaranteed quote, free). It does not explicitly say 'no side effects,' but the wording makes that 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?

Three short sentences, front-loaded with the 'Read first' directive, and every clause adds either scope, a caveat, or a return detail. 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?

For a parameterless, read-only explainer with no output schema, the description covers purpose, when to call, and what it returns (who receives details, consent wording, confirmation process). Nothing important is missing for an agent to call it correctly.

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 an empty schema, so there is no parameter meaning to add. The description focuses on the return content, which is the only input-free behavior an agent needs; baseline 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 names the exact action ('states plainly what submit_enquiry does') and the resource (Football Agent Index enquiry flow), making clear this is an informational read, not the submission tool itself. It also lists concrete output contents, so an agent will not confuse it with the sibling submit_enquiry.

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' is an explicit instruction to invoke this before acting, and the description ties it directly to submit_enquiry. It does not spell out when to avoid it or compare with checker_* siblings, so it stops short of full alternative-based 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 provided, the description carries the full behavioral burden. It discloses that the tool returns field metadata rather than performing a mutation, and it enumerates what information is available. It does not explicitly state that the tool is read-only, but the metadata-listing nature makes that sufficiently 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?

The description is two sentences with no filler. It front-loads the key metadata attributes, then ends with a concise, actionable usage note. 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?

For a tool with no parameters, no annotations, and no output schema, the description provides exactly what an agent needs: what the tool returns, the structure of that return, and how to use the returned keys. It is complete for this low-complexity 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 tool has no parameters, which earns a baseline of 4. The description adds useful semantic context by indicating that the returned field keys are the exact keys to use for submit_enquiry, which helps the agent connect this tool's output to the next step.

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 this tool exposes every field of the Football Agent Index enquiry along with metadata such as key, label, type, required status, help text, and allowed options. It differentiates itself from submit_enquiry by explaining that answers should be passed using these field keys, though it does not explicitly contrast with 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?

The second sentence gives actionable usage guidance: call this tool to discover field keys, then use those keys when calling submit_enquiry. This establishes clear context for when to use the tool, though it does not explicitly discuss when not to use it or name 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 provided, the description carries the full burden and fully delivers. It discloses the two-step validation/submission behavior, the email with a click-link requirement before providers see the enquiry, the exact consent statement, and the fact that this is not a purchase or guaranteed quote. These behaviors go far beyond what a one-line description would offer.

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 front-loaded with purpose and exclusions, then proceeds through steps in logical order. Every sentence conveys a distinct piece of necessary workflow information—step 1 behavior, step 2 condition, email requirement, and consent definition—with no filler or repetition.

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 tool's two-step complexity, no annotations, and no output schema, the description is remarkably complete. It explains what step 1 returns (summary, consent line, token), what the agent must do (show the person), when to proceed to step 2, and the post-submission email flow. An agent has enough information to invoke the tool correctly.

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 meaningful semantics: answers are 'keyed by field key from enquiry_fields', consent must be true only when the exact consent line has been read and agreed to, and confirmation is the token from step 1 used only in the second call. This workflow-level meaning is not present in 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?

The description opens with a specific verb and resource: 'Submits an enquiry to Football Agent Index' and immediately disambiguates with 'NOT a purchase, NOT a guaranteed quote.' It also gives the two-step workflow context, making it unmistakably distinct from sibling tools such as enquiry_fields, which supplies the field keys referenced here.

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 when-to-call guidance: Step 1 with answers and consent=true, then Step 2 only after the person agrees, using the returned confirmation token. It also points the agent to enquiry_fields for the answer keys and explains what to show the user, leaving no ambiguity about the proper invocation sequence.

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