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Python
Hybrid

List Effective Attack Paths

list_effective_attack_paths

Retrieve effective attack paths for a threat model, including missing or dangling path suggestions to identify coverage gaps.

Instructions

Effective AttackPath set + lifted missing/dangling suggestions.

AttackPaths inherit from ancestors with the same own / inherited provenance as other entities. The suggestions block is the missing-path / dangling-path delta computed against the composed effective topology — a child sees the inherited baseline claims, the composed reach surface, and the delta against both.

Return shape::

{
  model_id, flag_enabled,
  effective_paths: [{kind, qualified_id, owner_model_id,
    owner_title, origin, entity}, ...],
  lattice_positions: int,
  authored_paths: int,
  suggestions: {missing_path: [...], dangling_path: [...]},
}

When composition is disabled on the backend, effective_paths is empty, the counts are zero, suggestions is empty, 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

B3.4/5.0
Behavior4/5

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

With no annotations present, the description carries the full burden, and it delivers substantial behavioral detail: provenance-based inheritance, the suggestions delta computed against the composed effective topology, and the degenerate-case output when composition is disabled. It stops short of a 5 only because it never explicitly states the operation is read-only with no side effects, though 'list' makes that likely.

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 organized well: concept, computation semantics, structured return shape, then the disabled-composition edge case. It is dense but not wasteful. It loses a point because the opening fragment is cryptic and a single plain-language sentence stating 'returns the composed effective attack paths plus missing/dangling suggestions' would make it far more readable.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The genuinely hard part — what 'effective' means and how suggestions are computed — is explained well, and the return shape is documented despite the presence of an output schema. Clear gaps remain: the required server_version parameter is undocumented, no guidance selects this over overlapping read siblings, and the suggestion item structure is only sketched. Adequate for a domain expert, but an agent still faces uncertainty on parameters and tool selection.

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%: model_id is described by the schema while server_version has no description, and the tool description adds nothing about either parameter. server_version remains a complete mystery — no hint of valid values, semantics, or relationship to the backend — so the agent must guess what to pass for a required field.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description identifies a specific resource (the effective attack-path set) and the companion suggestions block (missing/dangling deltas), and the title/name supply the 'List' verb. The concept of 'effective' is defined via composition and provenance inheritance, which helps separate it from authored-path reads. However, the opening line is a jargon-heavy fragment rather than a plain statement of function, and it never explicitly contrasts itself with sibling list_effective_* tools.

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

Usage Guidelines3/5

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

Usage context is implied: an agent can infer this tool is for retrieving composed effective attack paths and their missing/dangling deltas. The 'when composition is disabled' clause gives a conditional under which output degrades, which is useful. But there is no explicit when-to-use/when-not-to-use guidance or naming of alternatives (e.g., list_effective_entities, get_reachability_verdicts, check_control_gaps), leaving sibling selection to inference.

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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