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Mipiti
by Mipiti

assume_control

Assign an assumption to mark a control as externally handled. The control remains active when the assumption is active and attested, with AI verification that the assumption plausibly covers the control.

Instructions

Mark a control as externally handled by an assumption.

Writes the assumption to group 1 as the sole member. The control counts as active for mitigation group completeness when the referenced assumption is active and attested.

AI relevance gate: the platform evaluates whether the assumption plausibly covers the control before saving. If the evaluator rejects, this tool raises with the rejection reasoning — there is NO override. Resolve by either picking an assumption whose description covers the control's responsibility, or by refining the chosen assumption's description to make coverage explicit. For compound (AND) or multi-path (OR) cases, use set_control_assumption_groups instead.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
model_idYesID of the threat model.
control_idYesID of the control (e.g., "CTRL-03").
assumption_idYesID of the assumption that covers this control.
server_versionYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

No annotations are provided, so the description carries the full burden. It describes the write operation (writes assumption to group 1), the AI relevance gate with no override, and the condition for the control to count as active. It does not mention idempotency or side effects, but it is fairly 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 concise and well-structured. It uses short paragraphs and bullet points effectively, front-loading the core purpose and then providing detailed guidance without unnecessary words.

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 the tool's complexity and the presence of an output schema, the description covers the essential behavioral aspects: the action, the effect on mitigation groups, the AI gate with resolution advice, and the alternative tool for compound cases. It is complete for an agent to correctly invoke the tool.

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 description does not elaborate on individual parameters beyond what the schema provides. Schema coverage is 75% (high), so baseline is 3. No additional value from description on parameter meaning.

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 starts with a clear, specific verb-resource phrase: 'Mark a control as externally handled by an assumption.' It immediately distinguishes itself from the sibling tool 'set_control_assumption_groups' by naming the alternative for compound or multi-path cases.

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

Usage Guidelines5/5

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

The description explicitly states when to use this tool versus 'set_control_assumption_groups' for compound/multi-path cases. It also provides guidance for handling rejection scenarios, telling the user to pick a different assumption or refine the description.

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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