1Lookup

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

It goes beyond the readOnly/destructive annotations by disclosing a material operational cost: 'Spends 1 credit per item.' It also notes the per-item result array, which is useful behavioral context even though an output schema exists.

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 deliver the core capability, the batch limit, the return shape, and the cost in order of importance. There is no redundant or filler 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 simple batch-validation tool, the definition covers what it does, the limit, the result shape, and the credit cost. The annotations and output schema fill in safety and return details; the only notable omission is explicit guidance on when to prefer single-item siblings.

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 input schema already documents both parameters, including the enum values and maxItems. The description adds little beyond restating the resource types and batch size, so a 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 a specific verb ('Validate') with concrete resources (phones, emails, IPs) and a clear batch constraint (up to 50 in one call). This distinguishes it from single-item sibling tools like validate_phone and verify_email.

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 bulk usage by saying 'in one call' and 'up to 50,' which helps an agent choose it over repeated single validations. However, it does not explicitly name alternative single-item tools or state when not to use 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?

The description adds meaningful behavioral context beyond the readOnlyHint=true and destructiveHint=false annotations: 'Free, spends no credits.' This is valuable for an agent deciding whether to call the tool, though it does not disclose other potential details like rate limits or caching.

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, efficient sentence that front-loads the core purpose before adding the cost and usage notes. No filler or redundancy; every clause adds relevant 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?

With an output schema present and zero parameters, the description covers all necessary context: what is returned (plan and credits), the cost implication, and the recommended use case. Nothing an agent needs to call this tool correctly is missing.

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 schema already fully documents everything. The description does not need to add parameter details; the baseline for a zero-parameter tool applies here.

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 a specific verb ('Get') and explicit resources ('plan and remaining credits') for a named entity ('1Lookup organization'). This clearly differentiates the tool from its siblings, which are all verification/lookup operations rather than account inspection.

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?

'Call before a large run' provides a clear, actionable trigger for using the tool. It does not explicitly name alternatives, but the account-focused purpose and mention of cost make it unambiguous when to invoke this tool versus the sibling verification 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?

Beyond the readOnlyHint and destructiveHint annotations, the description discloses the kinds of data returned and the non-obvious cost of the call ('Spends 1 credit'). This gives the agent useful expectations about both the result content and the side effect of consuming a credit.

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 efficient sentence with no filler. It front-loads the core operation, then adds the return categories and cost. Every phrase contributes useful 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?

For a single-parameter IP lookup with an output schema present, the description adequately covers the operation, the kind of results returned, and the credit cost. Nothing essential is missing for an agent to invoke 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?

Schema description coverage is 100%: the single 'ip' parameter is already documented as 'IPv4 or IPv6 address.' The tool description reinforces the parameter's role but does not need to add further detail. The baseline of 3 is appropriate because the schema already carries the semantic weight.

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, 'Look up an IP address,' and then enumerates concrete result categories: geolocation, connection type, proxy/VPN/Tor/datacenter flags, and fraud/risk score. This clearly differentiates it from sibling tools that handle phone/email verification or account/bulk operations.

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 intended use is clear from the resource itself: use it when an IP address needs enrichment or risk scoring. It does not explicitly list alternatives or exclusions, but the sibling set contains no competing IP-specific tool, so an agent can route to this tool without ambiguity. Mentioning that it 'Spends 1 credit' adds practical usage 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?

Annotations already indicate read-only, non-destructive behavior. The description adds non-obvious behavioral context beyond annotations: it spends 1 credit and returns specific attributes such as line type, carrier, DNC status, and fraud/risk score. This is useful operational transparency.

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 short, information-dense sentences. It front-loads the primary action, then states the outputs and the credit cost. No filler or redundant restatement of the tool name.

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?

With an output schema present and annotations covering read-only safety, the description covers the essential behavior, return attributes, and cost. It lacks explicit routing to sibling tools like verify_email or bulk_verify, but this is a minor gap given 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?

Schema description coverage is 100%, so the parameters 'phone' and 'country' are already well documented in the input schema. The description does not add parameter-level 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 uses a specific verb and resource ('Validate a phone number') and defines what validation means: confirming it is real and active. It also lists distinctive outputs (line type, carrier, DNC status, fraud/risk score), which clearly differentiates it from email verification and IP lookup 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?

The description gives clear context for when to use the tool: whenever a phone number needs to be checked for validity, activity, and associated metadata. It does not explicitly name alternatives or exclusions, so it misses the top score, but the intended use is unambiguous.

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 already cover readOnly and destructive hints, so the description adds meaningful behavioral context: the verification performs existence/deliverability checks and categories like disposable/role-based/risky. The 'Spends 1 credit' note adds cost behavior not present in annotations.

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?

Two concise sentences with no fluff. Every sentence contributes: functional scope and cost. The information is front-loaded and easily scannable.

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 single-parameter tool with an output schema and annotations covering safety, the description sufficiently covers behavior and cost. It is complete enough for an agent to select and 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?

Schema coverage is 100% and the email parameter already has a clear description. The tool description reinforces the purpose of the parameter but does not add new format or constraints beyond what the schema provides. 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 uses a specific verb ('Verify') and resource ('email address') and further defines the action: checks existence, deliverability, and flags disposable, role-based, and risky addresses. This clearly differentiates it from sibling tools like validate_phone and bulk_verify.

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 single-address verification and mentions 'Spends 1 credit,' hinting at a per-address cost versus bulk_verify, but it does not explicitly state when to use this tool over bulk_verify or other alternatives. No exclusions or alternative routing is given.

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