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mimaworks

mimaworks/governance-mcp

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by mimaworks

acknowledge_policy

Record and timestamp team members' acknowledgment of AI governance policies to generate verifiable audit evidence for EU AI Act, SOC 2, and ISO 42001 compliance.

Instructions

Record that a team member has read and understood an AI governance policy. Generates an immutable policy_acknowledged evidence record — required for EU AI Act Art. 9, SOC 2 CC1.4, and ISO 42001 A.5.1.

Use this when:

  • A new employee joins and needs to acknowledge the AI use policy

  • An AI policy has been updated and the team needs to re-acknowledge

  • A periodic renewal acknowledgment is due (annual policy reviews)

The record is timestamped to now and cannot be modified after creation. Auditors verify the person's email, policy version, and exact timestamp.

Pass dry_run=true to preview which controls this acknowledgment would earn without writing anything to the ledger. Use this before the real call to confirm the payload is correct.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dry_runNoIf true, preview which controls this acknowledgment would earn without writing to the ledger. record_id in the response will be the nil UUID. Use this before the real call.
policy_urlNoURL to the versioned policy document. Strongly recommended — auditors click through to verify what was acknowledged.
policy_nameYesHuman-readable policy name. E.g. 'AI Use Policy' or 'Model Risk Management Policy'.
system_nameYesThe AI system this acknowledgment applies to. Must match the system_name used in mima.attest(system_name=…).
person_emailYesEmail of the person acknowledging the policy. Must be a real individual — auditors trace acknowledgments to named persons (Art. 14).
policy_versionYesVersion of the policy being acknowledged. Auditors verify this. Use semantic versioning or a date, e.g. 'v3.1.0' or '2026-06-01'.
acknowledgment_typeNo'initial' = first time reading this policy. 'renewal' = periodic re-acknowledgment. 'update' = policy was revised and team is acknowledging the new version. Defaults to 'initial'.
Behavior4/5

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

With no annotations provided, the description carries the full transparency burden. It discloses key behaviors: the record is immutable, timestamped to now, and dry_run=true previews without writing. It also explains auditor verification. This is strong, though it omits details like permissions or failure modes.

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: a crisp main sentence, a bulleted list of use cases, then behavioral notes and dry_run guidance. Each section earns its place without redundant fluff, making it easy to scan and understand.

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?

For a tool with 7 parameters and no output schema, the description sufficiently covers purpose, usage, behavior, and even hints at response structure (e.g., nil UUID in dry_run). It lacks error handling or permission details, but overall it's complete enough for an agent to use effectively.

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 description coverage is 100%, so the schema already documents all 7 parameters. The description adds minimal extra parameter context (e.g., dry_run usage), but doesn't significantly enhance beyond the schema. Baseline 3 is appropriate.

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?

Description clearly states the tool's purpose: recording that a team member read and understood an AI governance policy. Uses a specific verb ('record') and resource ('policy_acknowledged evidence record'), and distinguishes itself from sibling tools like attest by focusing on policy acknowledgment for compliance.

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 includes a 'Use this when' section listing three concrete scenarios: new employee onboarding, policy updates, and periodic renewals. It also advises using dry_run before the real call but doesn't explicitly mention when not to use this tool, so it stops short of full exclusion guidance.

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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