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list_suspicious_process_events

Aggregate and list suspicious process event detections across your tenant for runtime evidence of compromise. Covers Runner-Worker-Memory-Read, Reverse-Shell, and Privileged-Container. Each result includes a clickable dashboard URL for investigation.

Instructions

List suspicious-process-event detections across the tenant. This is a virtual detection ID that aggregates three real types: Runner-Worker-Memory-Read (credential theft from runner memory), Reverse-Shell, and Privileged-Container. Use for runtime-evidence of compromise during an incident. Every result has a dashboard_url — when you present detections to the user you MUST include a clickable link per detection, not just the first one.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
customerNoStepSecurity customer/tenant identifier. Optional — if omitted, falls back to STEP_SECURITY_CUSTOMER env var. Returns detections aggregated across ALL GitHub orgs installed under this tenant.
statusNoDetection status filter. Defaults to 'new'.
limitNoMax detections to return (1-200). Defaults to 50.
orgScopeNoOptional: restrict to a single GitHub org under this tenant (uses the owner-scoped endpoint instead of tenant-wide).
Behavior3/5

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

No annotations provided, so description carries full burden. It discloses that it's a virtual detection ID aggregating three types and that results have a dashboard_url. Lacks explicit statement on read-only nature or mutability. Adequate but not rich.

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?

Two sentences, front-loaded with purpose and additional detail. Efficient, but the second sentence could be split for clarity. Every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, description explains dashboard_url but not other return fields. Parameter descriptions are thorough. Usage guidance is clear. Some room for improvement in specifying return structure.

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 has 100% coverage with descriptions for all 4 parameters. Description adds extra meaning: explains the virtual detection ID aggregation and instructs to present clickable links per detection, adding value beyond 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?

Describes a specific verb (list) and resource (suspicious-process-event detections), explains it's a virtual detection ID aggregating three real types, and distinguishes from siblings like list_detections and list_imposter_commit_detections.

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?

States explicit usage context: 'Use for runtime-evidence of compromise during an incident.' However, does not explicitly mention when not to use this tool versus alternatives (e.g., list_detections), but provides clear context for use.

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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