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Mipiti
by Mipiti

revalidate_threat_model_entities

Re-run quality validation on threat model entities to apply improvements or clear stale warnings. Non-destructive: only flagged entities get deeper review; controls and objectives carry forward.

Instructions

Re-run quality validation on a threat model's existing assets and attackers, as if they were freshly generated. A fast first-pass check judges every entity; only the ones it flags get a deeper review that confirms them, sharpens their wording, or flags them for you.

Use this to apply validation improvements to an already-generated model, or to clear stale quality warnings — without regenerating the whole model (which would destroy controls, assertions, and components). It is non-destructive: an entity that should be removed is left in place with a quality warning rather than deleted, so no control objective loses its asset or attacker anchor. The result is saved as a new model version; controls and control objectives carry forward.

May consume credits for the entities that need the deeper review; a model already in good shape costs nothing. Returns the updated model envelope: {"accepted": true, "model": {...}}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
model_idYes
server_versionYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

No annotations are provided, so the description fully discloses behavior: it is non-destructive (flagged entities left in place with warnings), saves a new version, carries forward controls, may consume credits, and returns a specific envelope format. This is comprehensive.

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 somewhat lengthy but well-organized, with clear sections for process, use cases, behavioral notes, and output. Every sentence adds value, though slight reduction could improve conciseness. It is front-loaded with the primary action.

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 existence of an output schema, the description covers purpose, usage, behavior, side effects (credits, non-destructive), and return format. It is complete for the complexity of the tool, leaving no major gaps.

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?

The input schema has 0% description coverage, but the description does not elaborate on the two parameters (model_id, server_version). While model_id is implied by context, server_version is not explained. The description should have provided meaning or allowed values for these parameters.

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 the tool re-runs quality validation on existing assets and attackers of a threat model. It distinguishes itself from sibling tools like generate_threat_model by specifying it does not regenerate the whole model, thereby avoiding destruction of 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?

The description explicitly tells when to use: 'apply validation improvements to an already-generated model' or 'clear stale quality warnings'. It contrasts with regeneration, indicating when not to use, and notes it is non-destructive, guiding appropriate context.

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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