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Mipiti
by Mipiti

revalidate_entity_quality

Re-runs quality validation on existing threat model assets and attackers, applying improvements and returning the updated model.

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_idYesID of the threat model whose assets and attackers to re-validate.
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 carries full burden for behavioral disclosure. It does so thoroughly: explains non-destructive behavior, that flagged entities are left with warnings rather than deleted, that controls carry forward, that a new model version is saved, that credits may be consumed, and what the returned envelope looks like.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured and front-loaded, beginning with a precise summary of the tool's purpose. Every sentence provides useful information: use cases, non-destructive behavior, versioning, cost implications, and return format. There is no filler or repetition.

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?

Despite having no annotations, the description covers the key operational context: when to use, side effects, cost behavior, persistence as a new model version, and the response envelope. This is sufficient for an agent to invoke the tool correctly.

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%, with server_version completely undescribed and model_id having a schema description. The tool description does not add meaning for either parameter; it only restates the model context already implied by model_id. server_version remains opaque, and no parameter-level guidance is provided.

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 a specific verb and resource: re-run quality validation on a threat model's existing assets and attackers. It also distinguishes itself from regenerating the model by emphasizing it is non-destructive and preserves 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 says when to use this tool: to apply validation improvements to an already-generated model or clear stale quality warnings without regenerating the whole model. It also warns against regeneration by noting what would be destroyed, giving clear guidance relative to sibling tools like generate_threat_model or refine_threat_model.

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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