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

With no annotations provided, the description carries the behavioral disclosure burden. It adds useful formula context: the 5.95% CPP rate, the 4% CPP2 rate, the CPP2 band concept, and fallback-to-defaults behavior for missing inputs. It leaves some ambiguity around employer versus employee perspective and does not clarify return formatting, but the core calculation behavior is disclosed.

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, front-loaded with the imperative verb, and uses a structured colon list to convey the calculation components. There is no filler or redundant explanation beyond briefly restating the title, and every sentence carries information.

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 has no output schema and no annotations, so the description must explain return behavior. It covers CPP and CPP2 outputs but never explicitly mentions EI despite 'EI cost' in the title, and it does not describe the returned result shape or how cppStatus=no interacts with the calculation. Useful defaults and rates are present, but the omission of EI is a meaningful completeness 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?

Schema description coverage is 100%, so the baseline is 3. The description adds formula-level context about CPP and CPP2 contributions, but it does not add meaning to the individual parameters beyond what the schema already provides. The fallback-to-defaults statement repeats schema default behavior rather than new parameter 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 uses a specific verb and resource: 'Run the Employer CPP and EI cost, 2026 calculator'. It lists concrete calculator outputs such as CPP contributory earnings, the 5.95% CPP contribution, the CPP2 band, and the 4% CPP2 contribution, making the intent clear and distinguishing this from sibling describe-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?

There is no guidance about when to use this tool versus alternatives like calculator_describe, enquiry_describe, or submit_enquiry. The description does not provide conditions, exclusions, or context to help the agent decide between calculate and the 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?

No annotations are provided, so the description carries the burden of behavioral disclosure. It does disclose that the tool returns the calculator's input metadata, outputs, assumptions, and tables, which implies a read-only describe operation. It does not explicitly mention side effects, safety, or return format, but for a describe tool this is a moderate 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 that packs in all essential information without wasted words. It front-loads the calculator context and specifies exactly what content will be included.

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 metadata tool with no parameters and no output schema, the description is nearly complete: it names the inputs, outputs, assumptions, and tables. It could be slightly stronger by explicitly noting that it returns documentation rather than a calculation result, but overall the agent has enough context to call it appropriately.

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 schema coverage is 100%, so the baseline is 4. The description adds useful context by mentioning units, ranges, defaults, outputs, assumptions, and tables, even though 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 the resource (the calculator) and the scope: its inputs, outputs, assumptions, and tables. It is distinguishable from the sibling 'calculate' because it describes the calculator rather than performing a calculation, though it does not explicitly say 'use this to get documentation'.

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 for learning about the calculator's inputs, outputs, and assumptions, which is useful context. However, it never explicitly states when to use this versus 'calculate' or other siblings, leaving the decision to the agent's inference.

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 transparency burden. It clearly discloses what submit_enquiry does not do (no purchase, order, payment, guaranteed quote), notes that it is free, and describes what is returned. This is strong behavioral disclosure for an information-style tool.

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 front-loaded with the most important cue ('Read first'). Every sentence adds distinct value: defining the tool, setting accurate expectations, and listing what the returned explanation covers. No filler or 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?

For a zero-parameter, no-output-schema describe tool, the description is fully sufficient. It explains the domain context, the nature of the process, key constraints, and the content of the returned explanation. An agent can accurately decide to invoke it and know what to expect.

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 there are no parameter ambiguities to document. The description appropriately focuses on behavior and output rather than inputs, matching the baseline for a no-parameter 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 states exactly what the tool does: it explains what submit_enquiry does in the Employer CPP EI Calculator context. It also draws clear boundaries by saying no purchase occurs, no quote is guaranteed, and the service is free, which prevents the agent from confusing this with an action tool.

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' implies this should be used before engaging with submit_enquiry, but it does not explicitly address when to use this versus sibling tools like calculate, calculator_describe, or enquiry_fields. Usage 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?

With no annotations, the description carries the full behavioral burden. It clearly indicates that this tool returns field metadata and points to submit_enquiry as the place where answers are passed, which implies this tool is a read-only introspection step. It does not explicitly state 'read-only' or describe response shape, but the content is strong enough for an agent to understand its role.

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?

One sentence efficiently front-loads the resource, lists the returned metadata attributes, and finishes with a workflow instruction. There is 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?

For a zero-parameter metadata tool with no output schema and no annotations, the description is complete: it says what is returned, the attributes included, and how the result connects to submission. An agent can invoke this tool and know what to expect and how to use 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, so the empty schema fully covers input requirements and the baseline is 4. The description adds useful semantic context by explaining that the returned field keys should be used to structure answers 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 specific resource—the Employer CPP EI Calculator enquiry—and enumerates the metadata it exposes (key, label, type, required, help text, options). It is not a tautology, but it stops short of explicitly differentiating itself from sibling tools like 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 description gives a practical usage hint: answers should be passed to submit_enquiry keyed by field key, implying this tool is a prerequisite for submissions. It does not explicitly state when to prefer this over enquiry_describe, calculator_describe, or calculate.

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 behavioral disclosure. It reveals that this is neither a purchase nor a guaranteed quote, requires explicit consent with the exact consent wording, returns a summary and confirmation token, and that the enquiry is only delivered after the recipient clicks an emailed link. This is comprehensive behavioral context.

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 into numbered steps with clear conditions. Key disclaimers are front-loaded, and every sentence contributes operational or semantic value. The length is justified by the two-step workflow and consent requirements.

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 step 2 triggers (submission and email link). Combined with the detailed parameter descriptions and explicit workflow, an agent has everything needed to invoke the tool correctly in both steps.

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 schema already documents parameters well. The description still adds value by specifying that answers are keyed by field key from enquiry_fields, that consent must be true, and that the confirmation token comes from step 1 and must accompany the same answers in step 2. This goes beyond the schema's basic property descriptions.

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 action (submits an enquiry), the target (human providers via Employer CPP EI Calculator), and explicitly distinguishes itself from a purchase or quote. The two-step nature is conveyed in the title and description, making it easy to separate from sibling tools like calculate.

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?

Provides an explicit, step-by-step invocation protocol: step 1 requires answers and consent=true, returns a token; step 2 requires the same answers plus the token only after the person agrees. It also clarifies the condition for calling the tool again and the consent requirement, leaving no ambiguity about when to use it.

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