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Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations are absent, so the description carries the full disclosure burden. It states the core output contract but says nothing about whether answering persists state, whether prerequisites exist (e.g., invoking checker_start first), how invalid 'choice' values are handled, or whether the 'final verdict' terminates the flow. For a stateful answer-submission tool, this is a significant 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 a single sentence, front-loaded with inputs then outputs, with zero filler. It earns its place efficiently, though it could have used the spare room to add a prerequisite or error-handling note.

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?

The tool sits in a multi-step flow (siblings checker_start and checker_tree), yet the description provides no flow context: how to begin, what invalid choices do, or what the response shape looks like—especially with no output schema. A minimal multi-question flow needs at least one of these clarified.

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?

With 0% schema description coverage, the description must define the parameters, and it partially does: it clarifies that 'question' is an ID (not text) and 'choice' is an option index (not a value). However, it does not specify the ID format, choice bounds, or zero-based versus one-based indexing beyond the schema's 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 uses a specific verb ('return') with concrete inputs ('question id', 'choice index') and outputs ('next question or the final verdict'), making the core purpose clear. The title reinforces this. However, it does not explicitly differentiate from sibling tools like checker_start or checker_tree, leaving the agent to infer its distinct 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?

There is no when-to-use guidance, no exclusions, and no mention of alternatives such as checker_start, checker_tree, or enquiry_describe. The need to call this after a session is started is only implied by the phrase 'next question'—an agent receives no explicit routing context.

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 must carry the full behavioral disclosure burden. It only says this is 'the first question' and does not disclose whether the tool returns a question, starts a session, resets state, 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 concise sentence with no unnecessary content, and it front-loads the core concept of being the first question. It is efficient, though it could add a bit more behavioral detail without becoming bloated.

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 annotations, no output schema, and no parameters, the description is still too thin. It does not explain what an agent should expect from calling this tool, what the 'question' looks like, or how to proceed to checker_answer or checker_tree.

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 and schema coverage is 100%, so there is no parameter information for the description to add. The described tool is simple enough that no parameter explanation is needed.

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 Prescription price checker decision guide, which is more specific than the title but still vague about what the tool actually does. It does not state a clear verb such as 'returns', 'presents', or 'initializes'.

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 this tool is used at the beginning of the guide, but it gives no explicit guidance on when to use it versus checker_answer, checker_tree, or other siblings. There is no mention of prerequisites or 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. It communicates that the tool exposes the full decision tree content and contains no language suggesting side effects or mutation. However, it does not explicitly describe the response format, whether the data is static, or any constraints such as size or pagination, 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, focused sentence. Every word contributes meaning: it identifies the resource, the domain, the scope, and the intended reasoning purpose. No filler or redundant information is present.

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 gives enough high-level context: it is the complete Prescription price checker decision tree for end-to-end reasoning. It does not specify the exact data structure returned, but the description's enumeration of contents mitigates that 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 input schema has zero parameters and 100% schema coverage, so there is no parameter ambiguity. The description does not need to explain parameters, and the baseline of 4 applies because there are no parameters to document.

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 what the tool provides: the complete decision tree of the Prescription price checker guide, including every question, option, and verdict. The 'whole decision tree' phrasing helps distinguish it from the step-oriented sibling tools checker_start and checker_answer, though it lacks an explicit verb like 'returns' or 'provides'.

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 an agent needs to reason over the entire guide rather than one step at a time. However, it does not explicitly state when to prefer checker_tree over checker_start or checker_answer, nor does it give exclusion criteria.

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 fully carries the behavioral burden, and it does so well. It discloses that this tool starts an enquiry with human providers, that nothing is bought or paid for, that no quote is guaranteed, that it is free, and that it returns consent wording and confirmation details. This gives the agent clear expectations about side effects and output.

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 reasonably concise and front-loaded with the 'Read first' instruction, followed by a clear summary of what the tool does and returns. It is slightly redundant with the title's 'not a purchase, not a guaranteed quote' phrasing, but the additional return-value detail 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 zero-parameter, no-output-schema informational tool, the description is complete. It explains the tool's purpose, what submit_enquiry does, what the user can expect, what information is returned, and how it should be used relative to the submission flow. An agent has everything needed to invoke and interpret this 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?

The tool has zero parameters and 100% schema coverage, so the baseline is 4. The description adds useful context about what the returned content will cover, but there are no parameters to document, so no additional parameter-level semantics are needed.

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, specific purpose: it describes what submit_enquiry does on Prescription Price Checker, with an explicit 'Read first' instruction. It differentiates itself from submit_enquiry by clarifying that no purchase, order, payment, or guaranteed quote is involved, so an agent can distinguish this informational tool from the sibling action 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?

'Read first' clearly signals that this tool should be used before submit_enquiry, and the description explains the nature of the enquiry so an agent knows what it is getting into. However, it does not explicitly state when not to use it or name alternatives beyond submit_enquiry, so it stops short of full exclusion 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?

No annotations are provided, so the description carries the full burden for behavioral context. It communicates that this is a metadata/introspection operation with no parameters and lists the information it exposes. It does not explicitly state that the tool is read-only, describe the output container (object vs array), or mention any authentication or access considerations, leaving some 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 dense sentence that front-loads the resource, enumerates the returned field attributes, and immediately connects the output to the submission step. Every part earns its place, with no redundant phrasing 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 zero-parameter metadata lookup with no output schema, the description covers the key content an agent needs and tells it how to use the results with submit_enquiry. It does not specify whether the result is a list or map, but the enumeration of per-field attributes and the key-based submission hint make the intended usage reasonably 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 schema confirms this with no properties. Since there are no parameters to document, the description does not need to compensate for schema gaps. The baseline of 4 applies because the parameter surface is entirely empty.

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 Prescription Price Checker enquiry') and enumerates the exact metadata returned: key, label, type, required, help text, and allowed options. It lacks an explicit verb like 'lists' or 'returns', which keeps it from a 5, but it is specific enough to distinguish this from an actual submission tool like 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 description gives one useful cross-reference: 'Pass answers to submit_enquiry keyed by field key', which tells the agent how to apply the output. However, it does not state when to prefer this tool over similar siblings such as enquiry_describe or checker_start, and offers no exclusion criteria.

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, and it delivers thoroughly. It discloses that step 1 only validates and returns a token, step 2 actually submits, an email with a link is sent, and the enquiry is only visible to providers after the link is clicked. The consent text is quoted verbatim, and the two-step side effects are fully visible.

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 longer than average, but every sentence earns its place given the complex two-step workflow. It is front-loaded with the key caveat about not being a purchase, then structured clearly into Step 1 and Step 2, making the flow easy to follow.

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 fully explains what step 1 returns (summary, consent line, confirmation token), what step 2 accomplishes, and the critical user-interaction requirement (show the person the summary, wait for agreement, email link click). An agent has everything needed to execute this 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?

Schema coverage is 100%, so the baseline is 3. The description adds extra meaning by explaining that the same answers must be used in step 2 and that the confirmation token unlocks submission. However, the schema already describes the consent text and the token's origin, so the added value is real but moderate.

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 Prescription Price Checker' and immediately clarifies what it is not ('NOT a purchase, NOT a guaranteed quote'). This clearly differentiates it from sibling tools like checker_answer, enquiry_describe, and enquiry_fields, which handle other parts of 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?

The description gives an explicit two-step protocol: call first with answers and consent=true, show the returned summary and consent line, then call again only if the person agrees, with the same answers and the confirmation token. It also defines exactly what consent means and references enquiry_fields for the answer keys, providing clear when-to-use and when-not-to-use guidance.

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