lookup_phoneValidate a phone number worldwide against its national numbering plan. Returns E.164, country, line type (mobile/fixed/VoIP/toll-free/premium) and a risk score. Works globally, not US-only.
| Name | Required | Description | Default |
|---|---|---|---|
| phone | Yes | Number in international form (+14155552671) or national form with country set | |
| country | No | ISO 3166-1 alpha-2, required only for national-format numbers | |
| strictness | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden for behavioral transparency. It discloses the output format and global scope, but does not explicitly state whether the tool has side effects, requires authentication, or imposes rate limits. The read-only nature is implied by 'validate' but not confirmed.
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, using two sentences to state the purpose and return values. It is front-loaded with the action and resource, and every word earns its place without unnecessary detail.
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 be self-contained. It lists the return values but not their structure, and it omits any discussion of error cases, the strictness parameter, or potential edge cases. The description is concise but under-specified for a tool with three parameters.
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 describes the phone and country parameters, so the description adds little beyond that. The strictness parameter (0-3) is completely undocumented in both schema and description, leaving the agent without guidance on how to set it or what it controls. With 67% schema coverage, the description should compensate but does not.
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 purpose with a specific verb and resource: 'Validate a phone number worldwide against its national numbering plan.' It also lists key return values (E.164, country, line type, risk score) and explicitly distinguishes itself from US-only tools and siblings that handle other entity types.
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 by noting it works globally and not US-only, which implies when to use it. However, it does not explicitly mention alternatives or exclusion criteria, nor does it state when another tool (e.g., verify_email) would be more appropriate. The relevance is clear but the guidance is implicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct resource or action: phone, device, domain, IP, user (aggregate), agent, and email. The descriptions clearly separate individual signal scoring from the combined user scoring, and cryptographic verification is uniquely assigned to verify_agent. No two tools overlap in purpose.
Most tools follow the score_<object> pattern (score_device, score_domain, score_ip, score_user), but lookup_phone and verify_email/verify_agent deviate by using different verbs. The naming is still readable and logically grouped, though a more consistent prefix like score_phone and score_email would improve uniformity.
Seven tools cover the core signals for signup risk assessment without unnecessary redundancy. Each tool serves a clear purpose, and the count is well within the typical 3-15 range for a focused API.
The set covers the primary data points used in fraud/risk evaluation: phone, IP, email, domain, device, and a combined user score, plus a cryptographic agent verification for advanced scenarios. There are no obvious dead ends or missing lifecycle operations for this read-oriented risk assessment domain.