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Mipiti
by Mipiti

Reevaluate Threat Model Factors

reevaluate_threat_model_factors

Re-run LLM factor judgment on every asset and attacker to re-baseline threat model factors after a fix, preserving controls and assertions. Each re-rating is recorded as an auditable revision.

Instructions

Re-run the LLM factor judgment on every asset and attacker in a threat model. Useful for re-baselining factors after a bug fix or feature-description change, without regenerating the whole model (which would destroy controls, assertions, components).

Each entity's factors and rationale are replaced with a fresh LLM-judged decomposition; the composed impact / likelihood is re-derived deterministically from the new factors. Each re-rating is recorded as a rating revision in the audit trail with change_reason (default: "LLM factor re-evaluation") so the starting-point regeneration is distinguishable from operator- supplied factor overrides via edit_asset / edit_attacker.

The platform's LLM factor judgment is a starting point. For deployment-specific factor adjustments (e.g., elevated regulatory_scope because your tenant is HIPAA-covered, or Commodity prevalence because your endpoint is public-internet exposed), use edit_asset / edit_attacker afterward with a change_reason documenting the operator override.

Per-entity soft-fail: an LLM failure on one entity is recorded in the response's failed_entities list (with id, kind, and reason); the remaining entities are still re-evaluated and their rating revisions persisted as they complete. The endpoint returns 503 only when every live entity failed — in which case nothing was persisted; retry when the evaluator is reachable.

Soft-deleted assets and attackers are skipped.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
model_idYesID of the threat model to re-rate.
change_reasonNoOptional override of the audit-trail reason (default: "LLM factor re-evaluation"). Use this to thread a higher-level reason like "Re-eval after refinement bug fix shipped in vN.N.N" when running the tool as part of a broader workflow.
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

A4.5/5.0
Behavior5/5

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

With no annotations, the description carries the full behavioral burden and does so thoroughly. It discloses that factors and rationale are replaced, that impact/likelihood is re-derived, that rating revisions are audited, the default change_reason, the soft-fail per-entity behavior, the 503 condition, and that soft-deleted entities are skipped. This is exemplary transparency.

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 long but well-structured: purpose first, then usage guidance, then behavioral details, then failure semantics. Each paragraph adds necessary information for a mutating re-rating operation. Minor redundancy and length prevent a 5, but the structure is strong.

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 covers purpose, alternatives, side effects, audit behavior, failure modes, and exclusion of soft-deleted entities. The presence of an output schema reduces the need to explain return shapes. However, the required server_version parameter remains unexplained, which is a real gap for correct invocation.

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 adds useful context for change_reason, explaining its default and audit-trail purpose, and reinforces model_id as the threat model to re-rate. However, server_version is a required parameter with no schema description and no explanation in the description, leaving its semantics unclear. The description partially compensates but does not fully cover the parameter set.

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 opens with a specific verb and resource: 'Re-run the LLM factor judgment on every asset and attacker in a threat model.' It clearly differentiates this from regenerating the whole model and from operator edits via edit_asset / edit_attacker, so an agent can identify when this tool is the right fit.

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?

It explicitly states when to use the tool: re-baselining after a bug fix or feature-description change. It also names the alternatives for other cases: use edit_asset / edit_attacker for deployment-specific factor adjustments. This is concrete, actionable routing guidance.

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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