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Mipiti
by Mipiti

list_effective_control_objectives

List effective control objectives for a threat model, classifying each as own, cross, or inherited. Use this to see all responsibilities before assessing coverage.

Instructions

Effective control objectives with origin classification.

Returns every CO visible on the effective model, each tagged with its origin: own (authored on this model), cross (an inherited CO whose asset or attacker is local to this model), or inherited (purely inherited from an ancestor). Use this to see what control objectives the model is on the hook for — including those it inherits — before reading coverage or reach.

Return shape::

{
  model_id, flag_enabled,
  control_objectives: [
    {co_qid, asset_qid, attacker_qid,
     security_properties: ["C"|"I"|"A"|"U", ...],
     origin: "own"|"cross"|"inherited"},
    ...
  ],
}

When composition is disabled on the backend, returns an empty list and flag_enabled: false.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
model_idYesID of the threat model.
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, so description carries full burden. It discloses the return shape with origin classification, and the edge case when composition is disabled (returns empty list and flag_enabled: false). It does not explicitly state that it is read-only, but the nature of the operation implies no side effects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with purpose and well-structured, but includes a long return shape example that may be redundant given the presence of an output schema (context signal: Has output schema: true). Could be more concise by relying on the output schema.

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 2 parameters and an output schema, the description covers purpose, usage context, behavioral edge case, and return shape. The only gap is the missing documentation for server_version, which slightly reduces completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 50%: only model_id has a description. The description does not mention server_version at all, leaving the agent unclear on what to provide. The description's return shape includes model_id but not server_version, missing an opportunity to clarify this parameter.

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 clearly states the verb 'list' and the resource 'effective control objectives' with a specific additional feature: origin classification (own/cross/inherited). It distinguishes from siblings like 'get_control_objectives' by highlighting the origin tags and the effective model scope.

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 explicitly says 'Use this to see what control objectives the model is on the hook for — including those it inherits — before reading coverage or reach,' providing clear context for when to invoke. However, it does not explicitly exclude use cases or mention alternatives, though the context is strong.

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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