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

With no annotations, the description carries the full burden. It discloses the screening nature, return format (candidates with links), and API cost. However, it does not explicitly state whether the tool is read-only, whether there are rate limits, or what happens when no matches are found. This is adequate but leaves some gaps.

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, highly efficient. The first sentence front-loads the core purpose and inputs, while the second adds essential caveats and cost. Every sentence earns its place; 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 matching tool with 5 parameters, no output schema, and no annotations, the description covers the purpose, inputs, output summary, and cost. It does not define the structure of 'evidence-backed candidates' or error handling, but it is sufficient for an agent to decide when to use it and what to expect. The lack of an output schema makes the return description slightly general, but still adequate.

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 meaningful context by stating that a match can be performed using 'brand, model, or UPC plus the product title', clarifying that product_name is required and that the others are alternatives. This goes beyond the schema's per-field 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?

The description clearly states the tool's function: 'Match one product against U.S. CPSC recalls and Product Safety Warnings.' It specifies the inputs (brand, model, UPC, product title) and the output (evidence-backed candidates with official notice links). This distinguishes it from the sibling tools (email_check, iban_check, vat_check, domain_check), which address entirely different domains.

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 the tool is used when you need to screen a product against CPSC recalls. It does not explicitly contrast with alternatives, but the sibling tools are clearly for other verification tasks, so the context is clear. The caveat 'A result is a screening candidate, not a safety or compliance decision' provides guidance on how to interpret results, earning it a 4.

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 of behavioral disclosure. It only says 'Show', which implies read-only, but it does not mention potential side effects, authentication requirements, or what happens if the balance is unavailable. It adds little beyond the tool name.

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 redundant words. It is front-loaded with the action ('Show') and directly states the resource. Every word contributes to the meaning.

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 is sufficient to understand its basic function. However, it does not describe the return format or any error behavior, which could be useful for a fully complete description. Given the trivial complexity, a score of 4 is appropriate.

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 description coverage is 100%. The baseline for 0 parameters is 4, and the description correctly does not attempt to explain parameters that do not exist.

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 ('Show') and names the exact resource ('current LookTwice workspace credit balance'). This clearly distinguishes it from sibling tools like email_check or domain_check, which focus on validation. 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 provides no guidance on when to use this tool versus alternatives. No exclusions, prerequisites, or alternative tool references are mentioned. The usage context must be inferred entirely from the tool name and description.

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 must fully disclose behavior. It mentions the four checks and the daily fair-use quota, which is useful, but it omits details about return format, error handling, or whether the checks are real-time. This is adequate but not comprehensive.

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-loading the action and resource before adding the quota context. Every word earns its place, with no redundant or irrelevant content.

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 one-parameter read-only tool, the description covers the core checks and quota, but since no output schema exists, it does not clarify what the response contains. This leaves some ambiguity about the tool's return value, making it only moderately 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 schema already fully describes the 'domain' parameter with an example, achieving 100% coverage. The description adds only 'registrable' as a qualifier, offering minimal additional meaning beyond the schema. Baseline 3 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 clearly states the tool checks a registrable domain's DNS, MX, TLS certificate, and RDAP registration state. This is a specific verb and resource, distinguishing it from siblings like email_check and vat_check.

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 the tool is used for domain health/registration checks but does not explicitly state when to use it or provide alternatives. No exclusions are mentioned, making the guidance implicit rather than explicit.

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 explicitly discloses a limitation: 'Does not probe the mailbox; mailbox_status is always not_checked' and also mentions cost: 'Costs 2 LookTwice API units.' These are valuable behavioral traits beyond basic function.

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 extremely concise, two sentences with no filler. It front-loads the core purpose and then adds critical behavioral information (mailbox status and cost) efficiently.

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 single-parameter tool with no output schema, the description covers purpose, validation scope, behavioral constraints, and cost. It is fully complete for the complexity level, requiring no 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?

Schema coverage is 100% with a single parameter 'email' described as 'The email address to check'. The tool description does not add additional parameter-level details beyond what the schema already provides, so baseline 3 is appropriate.

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 tool's function: 'Validate an email address' and enumerates specific checks (syntax, MX records, disposable-domain, role-account, free-provider signals), distinguishing it from sibling tools that validate other entities like IBAN or VAT.

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 clear context that this tool is for email validation, implying it should be used when an email address needs to be checked. It does not explicitly state when not to use it or mention alternatives, hence not a 5, but the context is sufficiently clear for selection.

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. It discloses core behaviors: validates structure/length/checksum, extracts bank identifier, does not verify account existence, and costs 1 API unit. It also includes a limitation. However, it does not describe the response format or error handling, which would enhance transparency further.

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 long, front-loaded with the primary action, and includes only essential information (validation details, extraction, limitation, cost). Every sentence serves a purpose, 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 simplicity (one parameter, no output schema), the description covers the main aspects: what is validated, what is extracted, the key limitation, and the cost. However, it does not specify the return format (e.g., boolean, object with details), which would be useful since there is no output schema. Still, it is largely complete for a validation 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 schema coverage is 100%, with the parameter 'iban' described as 'The IBAN to validate; spaces are allowed'. The description does not add new parameter-level details beyond rephrasing that it validates an IBAN. Since the schema already explains the parameter, the baseline of 3 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 uses a specific verb ('validate') and resource ('IBAN'), detailing exactly what is validated (country structure, length, MOD-97 checksum) and what is extracted (public bank identifier). It clearly distinguishes itself from sibling tools like email_check and vat_check by focusing on IBAN-specific functionality.

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 clearly implies when to use this tool (to validate an IBAN) and explicitly states a limitation ('Does not verify that the account exists or who owns it'), guiding the user away from using it for that purpose. However, it does not name alternative tools for account verification, so it lacks explicit 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?

No annotations are provided, so the description carries the full burden. It discloses the external service, real-time validity, conditional return of name/address, request evidence, and the cost of 1 API unit, providing rich 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 concise and well-structured: three sentences covering purpose, return values, and cost, with no extraneous 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?

Despite lacking annotations and output schema, the description explains the external service, what is returned, and the cost. It also notes the conditional availability of registered details, making the description complete for a simple validation 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?

Schema description coverage is 100%, with both parameters already well-documented (e.g., VAT number excludes country prefix). The description adds no additional parameter-specific detail, so the baseline score of 3 is appropriate.

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 specific verb 'validate', the resource 'EU VAT number', and the specific service 'official European Commission VIES service', clearly distinguishing it from sibling tools like email_check and iban_check.

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 clear context for when to use this tool: when validating EU VAT numbers against VIES. It does not explicitly name alternatives or exclusions, but the scope is unambiguous given the tool's name and description.

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