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Phone Lookup

phone_lookup
Read-onlyIdempotent

Validate and analyze phone number: country, region, carrier, line type (mobile/landline/VoIP), timezone, formatted versions. Use to verify phone legitimacy and detect fraud risks. Requires E.164 format (+1234567890). Companion OSINT identity-investigation tools: username_lookup (social-platform handle correlation), email_disposable (throwaway-mail signal on associated email). Free: 30/hr, Pro: 500/hr. Returns {valid, country, region, carrier, carrier_status, line_type, timezone, formats}. carrier is omitted from the wire when libphonenumber has no mapping for the region (US/CA/GB and other MNP-restricted regions); always read carrier_status — 'known' means carrier is present, 'unsupported_region' means we cannot identify the carrier (do not infer the number lacks one).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
numberYesPhone number in E.164 format: + followed by country code and number, no spaces or dashes. Examples: '+14155552671' (US), '+905551234567' (TR), '+442071234567' (UK). Wrong: '0555-123-4567', '(415) 555-2671'

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already declare readOnly/idempotent behavior, but the description adds substantial behavioral detail beyond that: carrier is omitted from the wire for MNP-restricted regions, carrier_status must be read to distinguish 'known' from 'unsupported_region', and explicitly warns not to infer the number lacks a carrier. This is valuable, non-obvious behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense and front-loaded with purpose, then expands into use case, format, companion tools, rate limits, return shape, and an important edge case. Every sentence earns its place, though it is somewhat long. A tight, well-structured description.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with one parameter and an output schema, the description covers return fields, edge cases (carrier omission, carrier_status semantics), rate limits, and usage context. The carrier_status nuance is essential for correctly interpreting results and is fully explained.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema covers 100% of the single parameter (number) with format and examples. The description repeats the E.164 requirement but adds no new syntax or nuance beyond what the schema already provides. Baseline 3 applies since the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb ('Validate and analyze') and resource ('phone number'), then enumerates attributes (country, region, carrier, line type, timezone, formatted versions). It distinguishes itself from sibling OSINT tools by framing it as a phone-specific lookup and referencing companion tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

States explicit use cases: 'verify phone legitimacy and detect fraud risks.' Mentions companion tools (username_lookup for social-platform handle correlation, email_disposable for throwaway-mail signal) as alternatives, giving the agent clear guidance on when to choose this tool over related ones. Also includes rate limits.

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

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TDQS

A4.5/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, with differences between lookup/search/scan/audit for each domain. However, some overlap exists (e.g., email_mx vs email_security_posture, scan_headers vs contrast_scan) which could cause occasional confusion. Overall, boundaries are well-defined.

Naming Consistency5/5

Tool names follow a consistent verb_noun pattern (e.g., cve_lookup, check_headers, bulk_cve_lookup) with all lowercase underscores. Variations like kev_detail or ssl_check are minor and still predictable. No chaotic mixing of conventions.

Tool Count4/5

54 tools is high but justified by the broad cybersecurity scope (CVE, ATLAS, D3FEND, Sigma, domain, email, IOC, scanning). Some redundancy exists (e.g., three email-related tools), but the count is not excessive given the API's comprehensive feature set.

Completeness5/5

The tool set thoroughly covers the threat intelligence and domain investigation lifecycle: CVE/KEV/exploit/CWE, ATLAS/D3FEND/Sigma, DNS/WHOIS/SSL/subdomains, email security, IOC enrichment, and active scanning. No significant gaps are apparent for the stated cybersecurity purpose.