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ContrastAPI

by com.contrastcyber

ATLAS Case Study Lookup

atlas_case_study_lookup
Read-onlyIdempotent

Look up a MITRE ATLAS case study — a documented real-world AI/ML attack incident. Each case study links a sequence of ATLAS techniques (techniques_used) to the incident. Default response is SLIM (description truncated to 240 chars); pass include='full' for the verbose narrative. Use this after atlas_technique_search to find which incidents have exercised a given technique. Drill into the full techniques_used array via bulk_atlas_technique_lookup in a single call (next_calls emits exactly that hint). Returns 404 when the id is not in the synced catalog. Free: 30/hr, Pro: 500/hr. Returns {case_study_id, name, description, techniques_used, next_calls}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
includeNoDetail level. Default (omit/empty) returns slim (description truncated to 240 chars). Pass 'full' for the verbose narrative — case-study descriptions can run 1-3KB.
case_study_idYesMITRE ATLAS case study id, format 'AML.CS####' (e.g. 'AML.CS0000', 'AML.CS0014').

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

TDQS

A4.9/5.0
Behavior5/5

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

Discloses several behavioral traits beyond annotations: slim vs. full response (truncation to 240 chars), the include parameter impact, rate limits (30/hr free, 500/hr Pro), 404 behavior, and the output shape including next_calls. This adds substantial context about response size, error handling, and throttling, going well beyond the readOnly/idempotent annotations.

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 information-dense but every sentence contributes: resource definition, parameter behavior, usage workflow, error behavior, rate limits, and output fields. It is well-structured, front-loaded with the tool's purpose, and avoids filler or repetition.

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 moderate complexity (2 params, output schema present), the description is complete: it covers return key fields, error responses, rate limits, and how to use companion tools. The output schema handles detailed return structure, so the description need not enumerate all fields, making this appropriately complete.

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

Parameters4/5

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

Schema coverage is 100%, so baseline is 3. The description adds meaningful semantics beyond the schema: explains that the default include is slim, that 'full' gives verbose narrative, and provides example ID format (AML.CS####). This helps the agent understand the intent of each parameter and the impact of the include choice, adding value over the raw 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 identifies the tool as a lookup for MITRE ATLAS case studies (documented AI/ML attack incidents) and states what each case study contains (techniques_used). It distinguishes from siblings by specifying the resource (case study vs. technique or search) and points to related tools for search and bulk lookup.

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?

Explicitly describes the intended workflow: use after atlas_technique_search to find incidents using a technique, and use bulk_atlas_technique_lookup to drill into techniques_used. Also notes the next_calls hint and the 404 error for invalid IDs, providing clear context for when to use this tool versus alternatives.

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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