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industrial-aiops

OT-AIops Energy

compliance_evidence_bundle

Read-only

Export governance evidence trail as a deterministic zip bundle for auditors, containing audit rows, chain verification, rules, and doctor summary.

Instructions

[READ][risk=low] Export the audit-evidence bundle (zip) for an auditor.

Packages the governance evidence trail into one deterministic zip:
audit_rows.jsonl (secrets already redacted upstream), chain_verification.json
(SHA-256 hash-chain walk result), rules.yaml (if present), doctor_summary.json
(non-probing config/secret-store facts), and manifest.json. Path is validated
(no '..' traversal; parent created 0700).

Args:
    out_path: Destination zip path (must end in .zip).
    since: Optional ISO-8601 floor on the audit row timestamp (inclusive).
    until: Optional ISO-8601 ceiling on the audit row timestamp (inclusive).

Returns dict: {path, row_count, chain{ok, checked, unhashed, ...}, files[],
    since, until}.

Example: compliance_evidence_bundle(out_path="/tmp/evidence.zip",
    since="2026-06-01T00:00:00+00:00").

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sinceNo
untilNo
out_pathYes
Behavior5/5

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

Beyond the readOnlyHint=true annotation, the description discloses deterministic packaging, upstream secret redaction, path traversal validation with '..' rejection, parent directory creation with 0700 permissions, and a complete return dictionary. These are nontrivial behavioral traits that meaningfully inform an agent about side effects and safety.

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 well-structured with a summary line, file list, argument definitions, return value, and example. Every sentence contributes functional knowledge without fluff, and the front-loaded [READ][risk=low] tag aids quick classification.

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?

Despite lacking an output schema, the description defines the exact return dict structure, all parameters, validation behavior, and a usage example. It is sufficiently complete for an agent to invoke the tool correctly and interpret its result.

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?

The schema provides no descriptions and has 0% coverage, so the description carries the full burden. It explains out_path must end in .zip, defines since/until as ISO-8601 inclusive bounds, and provides a concrete usage example. This fully compensates for the empty schema descriptions.

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 opens with a specific verb-resource pair ('Export the audit-evidence bundle (zip) for an auditor') and details the deterministic zip contents, clearly differentiating it from generic export or compliance reporting tools. It names the exact output artifact and its purpose.

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 phrase 'for an auditor' gives clear context on when to use the tool, and the detailed contents imply it is for governance/audit evidence packaging rather than general data export. It does not explicitly name alternatives or exclusions, but the context is strong enough to guide selection.

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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