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Mipiti
by Mipiti

list_effective_control_objectives

List all control objectives for a threat model, each tagged as own, cross, or inherited, to see what the model is responsible for including inherited objectives.

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?

With no annotations, the description carries the full burden. It discloses the return shape with origin types, explains the empty list behavior when composition is disabled, and implies read-only operation. However, it does not discuss permissions, rate limits, or side effects, which is acceptable for a simple list operation.

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

Conciseness4/5

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

The description is well-structured with a summary, origin explanation, usage context, and return shape example. It is informative but slightly verbose; some redundancy could be trimmed. Still, it's clear and easy to parse.

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?

Given the output schema exists, the description complements it well by explaining the meaning of origin and the behavior when composition is disabled. It covers the key aspects needed to use the tool effectively.

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 description coverage is only 50%; server_version lacks any description. The tool description does not clarify the purpose of server_version or add meaning beyond the schema. It relies on the schema to document model_id, leaving server_version ambiguous.

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 it returns effective control objectives with origin classification, distinguishing it from sibling tools like get_control_objectives. The verb 'list' and resource 'effective control objectives' are specific, and the emphasis on origin tags and effective model scope sets it apart.

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 explicit context for when to use this tool: 'before reading coverage or reach.' It implies a sequential workflow but does not explicitly state when not to use it or name alternatives. Still, it offers clear guidance on its role.

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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