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Python
Hybrid

List Effective Control Objectives

list_effective_control_objectives

List effective control objectives for a threat model with origin classification: own, inherited, or cross. Use it to identify all controls your model is accountable for, including inherited ones, 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

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.62.2
  2. Removedv0.62.1
  3. First observedv0.57.0

TDQS

A4.1/5.0
Behavior5/5

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

With no annotations provided, the description fully carries the behavioral burden. It explains what 'effective' means, defines all three origin values, gives the exact return shape, and discloses the edge case where composition is disabled (empty list with flag_enabled: false).

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: purpose, semantics, usage timing, return shape, and edge case each get their own section. It is slightly longer than strictly necessary, but the return-shape block is valuable given no inline output schema is shown.

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?

The description is largely complete for a simple list operation: it covers scope, origin semantics, return shape, and a backend-specific edge case. The main gap is the undocumented server_version parameter, which prevents full call confidence.

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 only 50%, and the description adds no parameter-level guidance. model_id is documented in the schema, but server_version has no description in either the schema or the description, so an agent cannot tell what values or format it expects.

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 uses a specific verb ('Returns') and a clear resource ('every CO visible on the effective model'), and adds a distinguishing origin classification ('own', 'cross', 'inherited'). This makes it easy to differentiate from related tools like get_control_objectives or get_effective_coverage.

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 states when to use the tool: 'Use this to see what control objectives the model is on the hook for... before reading coverage or reach.' It provides clear context, though it does not explicitly name alternatives or state when not to use it.

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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