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mimaworks/governance-mcp

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by mimaworks

derive_controls

Reveal governance coverage gaps for a described AI action, showing uncovered required controls, evidence record types, and risk tier, to guide attestation proposals before writing.

Instructions

Returns your current governance coverage gaps relative to the described action.

Provides: the action description as context, uncovered required controls (with which record types evidence them), the system's registered risk tier and Art. 14 status where deterministic from the ledger.

Use this data to reason about which record types the described action requires and why — then propose them via dry_run_attest before writing.

When art14_applicable is null, the system is not yet registered. Offer to run /mima:register-systems to resolve it.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
descriptionYesDescription of the AI action, feature, or architectural decision. Be specific about what the system does, who is affected, and whether humans are in the loop.
system_nameNoName of the AI system (e.g. 'loan-scoring-v2'). Resolves risk tier and Art. 14 status from the ledger.
ai_risk_tierNoExplicit risk tier assertion for unregistered systems. Use when system is not yet registered and you know the tier. high → art14_applicable=true (cold-start mitigation).
Behavior5/5

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

With no annotations provided, the description carries full burden. It transparently lists what it returns (action description, uncovered required controls, risk tier, Art. 14 status) and notes non-determinism ('where deterministic from the ledger'). It also explains the null art14_applicable behavior and suggests a resolution path, exceeding typical disclosure.

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?

Four sentences, front-loaded with the core purpose, followed by a structured list of outputs and actionable guidance. No filler or redundancy; every sentence earns its place.

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 no output schema, the description thoroughly explains return values (action description, uncovered controls, risk tier, Art. 14 status) and includes conditional behavior for nulls and a recommended next step. It is complete for an analysis tool with moderate complexity and good sibling context.

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?

The schema already provides 100% description coverage for all three parameters, including detailed guidance for ai_risk_tier ('high → art14_applicable=true'). The tool description adds no extra parameter-level information beyond what the schema already states, so it hits the baseline for high schema coverage.

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: 'Returns your current governance coverage gaps relative to the described action.' This clearly distinguishes it from siblings like attest, get_posture, and dry_run_attest by focusing on gap analysis rather than attestation or overall posture.

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 provides clear context for use: 'Use this data to reason about which record types the described action requires and why — then propose them via dry_run_attest before writing.' It also gives a conditional for unregistered systems ('Offer to run /mima:register-systems to resolve it'). While it doesn't explicitly list when-not or alternative tools, the workflow is clear.

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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