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ContrastAPI

by com.contrastcyber

Subdomain Enum

subdomain_enum
Read-onlyIdempotent

Discover subdomains using passive methods: Certificate Transparency logs + DNS brute-force (no active probing). Use to map organization's attack surface; non-intrusive. Response carries next_calls — capped at 5 ssl_check hints (one per first-five subdomain) so triage scales to large enumerations without token bloat; pull tail entries by name when needed. Free: 30/hr, Pro: 500/hr. Returns {domain, count, subdomains, sources, found_via_wordlist, found_via_crtsh, wildcard_status, crtsh_status, warnings, summary, next_calls}. Always check wildcard_status FIRST: 'absent' means the DNS brute-force plane was meaningful; 'present' means the zone answers every name (wildcard DNS) so wordlist results were discarded and count is a certificate-transparency LOWER BOUND — report the surface as UNKNOWN, not small, and never infer low exposure from it; 'undetermined' means a negative-control probe went unanswered, so count is unverified. Then check crtsh_status: 'ok' means the CT lookup completed (so a low count is real); 'timeout' / 'rate_limited' / 'unavailable' / 'error' means CT logs did not respond and the count is wordlist-only — the actual attack surface is likely larger, retry later or surface the limitation to the user.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
domainYesRoot domain to enumerate subdomains for (e.g. 'example.com', 'tesla.com')

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

TDQS

A4.4/5.0
Behavior5/5

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

The annotations already indicate readOnly/idempotent/non-destructive, but the description adds crucial behavioral context: wildcard_status interpretation (absent/present/undetermined), crtsh_status handling (timeout/rate_limited/unavailable/error), and next_calls cap. This explains how to interpret potentially misleading counts, far exceeding the structured annotations.

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 long but well-structured: purpose is front-loaded, followed by usage, rate limits, output fields, and detailed status logic. Every sentence serves a purpose, though some redundancy with the output schema could be trimmed; still, it's concise enough for the complexity it covers.

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?

Given the tool's complexity and the presence of an output schema, the description fully covers edge cases like wildcard DNS and CT service failures, and explains how to interpret counts. It even warns against inferring low exposure from wildcard-present results, making it complete for an agent to use effectively.

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?

The schema covers the single 'domain' parameter at 100% with its own description, and the tool description does not add extra parameter-level meaning. Baseline applies since schema does all the heavy lifting; no additional format or constraint details are provided.

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 states 'Discover subdomains using passive methods: Certificate Transparency logs + DNS brute-force (no active probing)' with a clear verb and resource. It further distinguishes for attack surface mapping, separating it from sibling tools like dns_lookup or ssl_check.

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

Usage Guidelines4/5

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

Explicitly says 'Use to map organization's attack surface; non-intrusive', giving a clear when-to-use. It also provides rate limits and next_calls usage for large enumerations, but does not explicitly name alternative tools or state exclusions, so it lacks explicit when-not guidance.

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.

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