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AIops-tools

compliance-aiops

query_audit_events

Retrieve audit events from connected tools with filters on source, skill, status, risk level, and approval. Use to gather compliance evidence across your AIops agents.

Instructions

[READ] Cross-tool audit event query — the workhorse.

Args: source: Restrict to one source tool (e.g. nutanix-aiops). skill / tool / status / risk_level: Field filters. approved: True = only ops with an approver; False = only ops without one. selector: Evidence class filter — audit_trail / attribution / change / enforcement / exception. since / until: ISO timestamps bounding the period. limit: Max rows to return.

Returns {"events": [...], "count": N, "returned": N, "limit": L, "truncated": bool}. When truncated is true more events matched than were returned — narrow the filters or raise the limit. Never characterise the trail (counts, "no violations found") from a truncated result.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
toolNo
limitNo
sinceNo
skillNo
untilNo
sourceNo
statusNo
approvedNo
selectorNo
risk_levelNo
Behavior4/5

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

Without annotations, the description carries the full burden. It labels the tool as read-only, explains the return format including 'truncated' flag, and warns against characterizing from truncated results. Missing auth/rate limit info, but otherwise transparent.

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 highly concise: a one-line summary, a bulleted arg list, and a return description with warning. No redundant sentences, well-structured for quick scanning.

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 10 parameters, no enums, and no output schema, the description covers all necessary aspects: purpose, all parameter semantics, return format, and a critical behavioral caveat (truncation). Complete for effective use.

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

Parameters5/5

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

With 0% schema coverage, the description provides detailed explanations for all 10 parameters (e.g., 'source', 'approved', 'selector') that the input schema lacks. Adds meaning and usage context beyond type and default.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly indicates a cross-tool audit event query with '[READ]' and 'the workhorse'. It distinguishes itself from sibling tools like 'undo_apply' and 'activity_timeline' by being a general query tool, though it could be more explicit about its scope.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance on when to use this tool versus alternatives such as 'activity_timeline' or verification tools. The description only warns about truncated results, which is a behavioral note, not a usage comparison.

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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