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ContrastAPI

by com.contrastcyber

Tech Stack CVE Audit

tech_stack_cve_audit
Read-onlyIdempotent

Composite tech-stack + CVE audit (MCP-only, no REST endpoint). Detects technologies on the target domain, queries CVE database for known vulnerabilities per product, enriches top-10 CVE candidates with CISA KEV federal patch deadlines, and checks public exploit / PoC availability. Identical for every tier — all data is sourced from local DB mirrors (no Shodan/AbuseIPDB), so there is no tier gating. CVE candidate batch: 50. Cost: 10 tokens per call — Free 30/hr ≈ 3 audits, Pro 500/hr ≈ 50 audits. Returns {domain, technologies, cves_by_tech, kev_findings, exploit_findings, summary, next_calls}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
domainYesTarget domain to fingerprint and CVE-audit (e.g. 'example.com'). IPs and internal hostnames are rejected.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

TDQS

A4.5/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, and the description consistently aligns while adding substantial context: it specifies data is sourced from local DB mirrors (no Shodan/AbuseIPDB), the CVE batch size is 50, costs 10 tokens per call, and lists exact return fields. This goes well beyond the annotation baseline.

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

Conciseness5/5

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

The description is efficiently structured: it begins with the tool's composite nature, then lists steps, tier behavior, batch size, cost/rate, and return structure. Every sentence carries meaningful information with no filler, making it appropriately sized for a complex composite tool.

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 this complexity—composite logic, multiple outputs, and operational constraints—the description is exceptionally complete. It covers data sources, tier behavior, cost/rate limits, batch size, and return fields. The presence of an output schema reduces the need to explain returns, yet the description still does so, leaving no significant gaps.

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 single parameter 'domain' is already fully described in the schema, including an example and rejection of IPs/internal hostnames. The description does not add additional semantic detail about the parameter beyond what the schema provides, so the baseline of 3 applies due to 100% schema coverage.

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 'Composite tech-stack + CVE audit' and precisely enumerates the steps: technology detection, CVE querying, KEV enrichment, and exploit/PoC checks. This clearly identifies the tool's specific purpose and distinguishes it from sibling tools like cve_lookup or tech_fingerprint by highlighting the composite nature.

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?

The description provides strong contextual guidance, stating the tool is MCP-only, has no tier gating, and details cost/rate limits (e.g., Free 30/hr ≈ 3 audits). It clarifies when the tool is relevant—when a combined tech-stack and CVE audit is needed—but does not explicitly name alternatives or state 'use X instead when...'.

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.

Resources