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ContrastAPI

by com.contrastcyber

Sigma Rule Lookup

sigma_rule_lookup
Read-onlyIdempotent

Look up a single Sigma detection rule by UUID from the SigmaHQ corpus (~3,200 rules, refreshed daily at 02:00 UTC). Returns the full rule with title, description, status (stable/test/experimental/deprecated/unsupported), level (informational/low/medium/high/critical), logsource (product/category/service), detection logic, tags (including attack.t#### ATT&CK technique refs and cve.YYYY-#### CVE refs), author, references, and modification date. Use to fetch a known rule for context (e.g., a SIEM detection that fired) or to inspect a rule discovered via REST sigma_rule_search. When a rule tags an ATT&CK technique or CVE, the response next_calls surfaces atlas_technique_lookup / cve_lookup as natural follow-ups. Free: 30/hr, Pro: 500/hr. Returns {rule, next_calls}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
rule_idYesSigma rule UUID (RFC 4122, 36 chars, hyphenated). Example: '195e1b9d-bfc2-4ffa-ab4e-35aef69815f8'. Obtained from the REST sigma_rule_search endpoint or external SIEM correlation.

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?

Despite annotations already declaring read-only, idempotent, and non-destructive, the description adds substantial behavioral context: data refresh schedule (daily at 02:00 UTC), rate limits (Free/Pro), and the shape of the response including next_calls suggestions. This goes beyond the annotations and fully informs the agent.

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 dense but every sentence earns its place: purpose, return contents, use cases, follow-ups, rate limits, and response shape. It is well-structured and front-loaded, with no redundancy.

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 lookup tool with a rich output schema, the description covers the lookup target, return payload, usage context, rate limits, and related follow-up tools. It is complete enough for an agent to decide when and how to invoke this tool, and what to expect in response.

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 input schema already documents rule_id with type, format, example, and source (REST sigma_rule_search), so coverage is 100%. The description reinforces that the rule is a UUID and mentions the source, but adds no new parameter-level semantics beyond the schema.

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 clearly states the tool performs a lookup of a single Sigma detection rule by UUID, explicitly naming the resource (SigmaHQ corpus) and differentiating from bulk operations via the word 'single'. It also enumerates returned fields, making the purpose unmistakable.

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 explicit use cases: fetching a known rule for context or inspecting a rule found via REST sigma_rule_search. It also mentions natural follow-up lookups. However, it does not explicitly mention when NOT to use it (e.g., when needing multiple rules, use bulk_sigma_rule_lookup), so clear exclusions are missing.

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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