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Mipiti
by Mipiti

refine_control

Refine a control's description and justification; AI verifies collective sufficiency against mapped control objectives and supersedes prior assertions.

Instructions

Refine a control's description with AI-gated CO sufficiency check.

Two modes:

  • Provide description: proposes a new description directly.

  • Provide codebase_findings: the platform proposes a description based on existing code that may already satisfy the control.

  • Both can be provided: the platform evaluates the proposed description with the codebase findings as context.

The AI evaluates whether the mitigation group still collectively satisfies all mapped control objectives. If rejected, returns {accepted: false, reason, per_co} with per-CO reasoning.

Side effect on accepted refinements: every assertion attached to this control is superseded — their claims were authored against the prior description and are not guaranteed to align with the new one. The response includes superseded_assertions: <count> so the caller knows how many. Re-submit any assertion that still applies under the new description; superseded rows remain in history with superseded_by="control_refined:...".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
model_idYesID of the threat model.
control_idYesID of the control to refine (e.g., "CTRL-03").
descriptionNoProposed new control description (optional if codebase_findings provided).
justificationNoWhy this refinement is appropriate (min 10 chars).
server_versionYes
codebase_findingsNoDescription of existing code that may already satisfy this control's objective (optional). When provided without description, the platform proposes a description.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

No annotations provided, so description bears full burden. Discloses side effect: superseded assertions, including count in response and history marker. Also explains rejection with per-CO reasoning. Could mention authorization needs or rate limits, but current disclosure is strong.

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?

Highly concise and well-structured: a single sentence for purpose, then bullet-like mode descriptions, then side effect. Every sentence adds value, no redundancy.

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 complexity (multiple modes, side effects) and presence of an output schema, description covers all necessary aspects: acceptance/rejection outcomes, per-CO reasoning, and superseded assertions count. Agent can correctly invoke and interpret results.

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?

Even though schema coverage is 83%, description adds meaningful context: explains interaction between description and codebase_findings parameters, and the two modes. This goes beyond the individual parameter 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?

Clearly states the tool refines a control's description with an AI-gated sufficiency check. Describes two distinct modes (direct description, codebase findings) and combined mode. Distinguishes from sibling tools like update_control_status by focusing on description refinement with evaluation.

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?

Provides explicit guidance on when to use each mode (description, codebase_findings, or both). Explains that the AI evaluates if mitigation groups satisfy control objectives. Lacks an explicit 'when not to use' statement, but context 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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