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atlas_case_study_search

Read-onlyIdempotent

Search real-world AI/ML attack incidents from the ATLAS database using keywords or technique IDs. Retrieve case study summaries to explore adversarial techniques in practice.

Instructions

Search ATLAS case studies (real-world AI/ML attack incidents) by keyword or referenced technique. Default response is SLIM (description truncated to 240 chars per row); pass include='full' for the verbose summary. Useful when the user has a technique in hand and wants to see incidents that exercised it. Drill via atlas_case_study_lookup for the full procedure list. Free: 30/hr, Pro: 500/hr. Returns {query, total, results [{case_study_id, name, description (truncated by default), techniques_used}], next_calls}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keywordNoSubstring match against case study name + description (case-insensitive). Min 2 chars. Example: 'evasion', 'data poisoning'. Omit to list all.
technique_idNoFilter to case studies that include this ATLAS technique id, format 'AML.T####' or 'AML.T####.###' (e.g. 'AML.T0051'). Omit for any technique.
limitNoMax results to return. Range: 1-200.
includeNoDetail level. Default ('') returns slim records (description truncated to 240 chars). Pass 'full' for full description on every row.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

Discloses default slim response, includes truncation behavior, rate limits (30/hr Free, 500/hr Pro), and the return shape including next_calls. This adds value beyond readOnlyHint and idempotentHint 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?

Five sentences covering purpose, response format, usage context, rate limits, and return structure. Efficient and well-organized with no fluff.

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 presence of an output schema, the description still provides needed context: default behavior, sibling tool, rate limits, and return shape. Complete for a search tool.

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 descriptions cover all 4 parameters with 100% coverage. The description does not add significant extra meaning beyond what the schema provides, falling to baseline 3.

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 it searches ATLAS case studies by keyword or technique, and distinguishes itself from atlas_case_study_lookup by mentioning it returns slim results and directs to lookup for full procedure.

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 advises when to use it ('user has a technique in hand') and mentions drilling into atlas_case_study_lookup for full procedure. It lacks explicit when-not-to-use but is clear enough.

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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