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

No annotations are present, so the description carries the behavioral disclosure burden. It discloses that the tool calculates specific cost outputs and that missing inputs fall back to defaults, which is useful. However, this fallback behavior is already evident from schema defaults, and the description does not disclose return formatting, side effects, or units.

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 effectively front-loaded with a colon-led list of outputs, followed by a useful fallback note. Every sentence contributes relevant information, though the structure could be slightly more polished.

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 9 parameters, no annotations, and no output schema, the description provides the output categories but leaves return shape, units, and interpretation details unspecified. It also omits any relationship to sibling tools, so an agent must rely on inference; adequate overall but with clear gaps.

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 100%, so the baseline of 3 applies. The description adds no parameter-level meaning beyond the schema; it mentions output dimensions like 'per flight hour' and 'per year', but these are outputs, not parameter explanations.

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 and resource, 'Run the Jet operating cost model calculator', and clearly enumerates the four outputs it produces. It does not explicitly distinguish itself from sibling tools, but those are describe/enquiry tools, so the purpose is unambiguous.

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 offers no explicit when-to-use guidance or alternative routing, but the note that missing inputs fall back to documented defaults implicitly tells callers they can invoke the tool with partial input. Sibling tool names suggest the context, but no direct exclusions or conditions 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?

With no annotations provided, the description carries the burden of explaining behavior. It discloses the informational payload (inputs, outputs, assumptions, tables), which is good, but it does not explicitly state that the tool only describes and performs no calculation or side effects. The name 'describe' partially compensates.

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 scoped sentence fragment that front-loads the core content categories. Every word contributes value, and there is no redundancy 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 simple, parameterless describe tool without an output schema, the description adequately covers what an agent will learn: inputs, outputs, assumptions, and tables. It could be slightly more explicit about the return format or that this is documentation, but overall it is sufficient for the tool's simplicity.

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 there are no parameters that need explanation. The description refers to the calculator model's inputs, not tool parameters, which is appropriate for a describe-style tool.

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's content: the calculator's inputs with units/ranges/defaults, outputs, assumptions, and tables. It does not contain an explicit verb like 'returns' or 'describes,' and it does not verbally distinguish itself from calculate, but the title and sibling names make the purpose reasonably clear.

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 used when one needs the calculator's inputs, outputs, assumptions, or backing tables, especially versus calculate. However, it never explicitly states when to use this tool instead of alternatives, and it gives no exclusions or 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It does this well by stating that the enquiry is free, non-binding, and does not guarantee a quote, while also disclosing what the tool returns: who receives the details, consent wording, and confirmation method. This gives the agent accurate expectations about the tool's non-committal, informational nature.

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 'Read first,' which is a valuable directive, and then packs scope, exclusions, cost, and return values into a few sentences. Each sentence contributes meaningful information. It is slightly dense but appropriately sized for an informational tool.

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 tool with no output schema, the description covers the essential context: what the tool explains, what it does not do, that it is free, and what information it returns. It does not list every recipient or the exact consent wording, but that is acceptable because the tool exists to provide such details on demand.

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 is empty with 0 parameters, so there are no parameter semantics to clarify. Baseline for 0 parameters is 4, and the description adds relevant context by framing the tool as an explainer rather than an action-taking operation. No parameter documentation 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 clearly states that enquiry_describe 'States plainly what submit_enquiry does on Jet Operating Cost,' establishing it as an informational companion to the actionable sibling. It is not a tautology and distinguishes itself by clarifying that nothing is bought, ordered, paid, or guaranteed. However, it leans heavily on explaining submit_enquiry rather than explicitly saying 'this tool returns a description,' so purpose clarity is strong but not maximally direct.

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 instruction 'Read first' implies that the agent should consult this tool before using submit_enquiry, which provides some usage sequencing. However, it does not explicitly mention when not to use this tool or name alternatives such as calculate or enquiry_fields. Usage guidance is therefore implied rather than fully articulated.

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 burden of explaining behavior. It discloses what the tool provides: an exhaustive set of field metadata, including allowed options. It also implies a read-only informational purpose, though it never explicitly says it has no side effects or describes the output format. It adds meaningful context beyond the tool name, but stops short of full behavioral disclosure.

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. The first sentence enumerates exactly what metadata is returned, and the second provides the actionable relationship to submit_enquiry. It is front-loaded with the resource description and ends with practical usage guidance.

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 metadata tool, the description is largely complete: it explains the returned fields and how to use them. The only minor gap is that it doesn't state the output format or explicitly confirm the tool is read-only, but given the low complexity and clear wording, these are small omissions.

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 takes zero parameters and schema description coverage is 100%, so the baseline is 4. There are no parameter semantics to clarify; the description appropriately focuses on what the returned fields contain and how they should be used as keys for submit_enquiry.

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's resource: all fields of the Jet Operating Cost enquiry, including their metadata such as key, label, type, required status, help text, and allowed options. It doesn't use an explicit verb like 'get' or 'list', but the intent is unmistakable and it distinguishes itself from submit_enquiry by describing the data structure rather than submitting answers.

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 practical guidance: fields are to be discovered here and answers passed to submit_enquiry keyed by field key. This establishes a clear workflow context and relationship to a sibling tool. It doesn't mention exclusions or alternatives such as enquiry_describe or calculate, but the usage context is clear enough for an agent to know when to call it.

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, and it delivers: it explains validation, the returned summary/consent line/confirmation token, the consent condition, the email with a click link, and the exact consent wording. It also shows the human-in-the-loop ordering that an agent needs to follow.

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 well-organized with clear 'Step 1' and 'Step 2' labels, front-loaded exclusions, and no wasted sentences. The quoted consent text is repeated, but it is operationally necessary for the agent to display it verbatim.

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 two-step, stateful submission tool with no output schema and no annotations, the description is remarkably complete: it covers the first call, the returned token, the confirmation call, the email follow-up, and the consent requirement. An agent can invoke the tool correctly without needing additional context.

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 covers 100% of parameters, so the baseline is 3. The description adds meaningful step semantics: answers are keyed by field key from enquiry_fields, consent must be true only after explicit agreement to the quoted text, and confirmation is the token returned from step 1. This goes beyond the schema.

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 Jet Operating Cost') and immediately clarifies what it is not: NOT a purchase, NOT a guaranteed quote. This clearly differentiates it from the calculator-style siblings and makes the tool's role 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?

The description gives explicit two-step usage instructions: call without the confirmation token first, then call again with it only if the person agrees. It also excludes purchase/quote use cases. It does not explicitly name sibling tools as alternatives, so it misses the highest bar for routing.

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