Skip to main content
Glama
Mipiti
by Mipiti

reevaluate_threat_model_factors

Re-run LLM factor judgments on all assets and attackers to re-baseline a threat model after feature changes or bug fixes, preserving controls and avoiding full regeneration.

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

Behavior5/5

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

With no annotations, the description fully discloses behavioral traits: replaces factors and rationale, re-derives composed scores, records audit trail with change_reason, distinguishes from operator overrides, and details partial failure handling with failed_entities list and 503 status.

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?

Well-structured and front-loaded with main purpose. Each paragraph adds value, but could be slightly more concise. Still very effective.

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

Completeness5/5

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

Given the complexity and presence of an output schema, the description covers return behavior, side effects, and error handling thoroughly. No gaps identified.

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?

Schema coverage is 67% (2 of 3 params documented). Description adds meaning for change_reason (default and override usage) but does not cover server_version. Baseline 3 with moderate extra context.

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?

Clearly states the tool re-runs LLM factor judgment on all assets and attackers, and distinguishes it from regenerating the whole model by specifying that regeneration would destroy controls, assertions, and components.

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?

Explicitly states when to use (re-baselining after bug fix or feature change) and when not to use (for deployment-specific adjustments, recommending edit_asset/edit_attacker instead). Also explains soft-fail behavior and error scenarios.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Mipiti/mipiti-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server