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Mipiti
by Mipiti

edit_asset

Update an existing asset's fields, including identity and risk factors. Identity changes undergo LLM approval; factor edits require a change reason for audit trail.

Instructions

Edit an existing asset. Only provided fields changed.

The composed impact is server-derived from the factor fields; there is no way to set it directly. To change the rating, set factor values (the platform composes the new rating) and supply change_reason documenting the operator override of the LLM-generated factors. The reason is captured in the rating-revision audit trail.

LLM-gated on identity-bearing fields (name, description, security_properties). Factor and notes edits skip the gate.

Outcomes when identity fields change:

  • Accepted edit (LLM classifies as preserve) — normal envelope response.

  • Rejected edit (LLM classifies as replace / ambiguous) — {"accepted": False, ...}; nothing saved. Soft-delete + add-new instead.

Editing a soft-deleted asset is rejected — restore_asset first. 503 on evaluator outage, 502 on malformed response, 400 when factor fields are sent without change_reason.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNoNew name (optional).
notesNoNew notes (optional).
asset_idYesID of the asset (e.g., "A1").
model_idYesID of the threat model.
descriptionNoNew description (optional).
blast_radiusNo"Isolated" | "Multiplicative" | "Cascading".
change_reasonNoRequired when any factor field is supplied — documents the operator override of LLM-generated factors for the audit trail.
recoverabilityNo"Trivial" | "Manageable" | "Permanent".
server_versionYes
usage_subscoreNo"None" | "Low" | "High".
impact_rationaleNoNew rationale (optional).
regulatory_scopeNo"None" | "Notification" | "Legal".
integrity_subscoreNo"None" | "Low" | "High".
security_propertiesNoComma-separated properties (optional).
availability_subscoreNo"None" | "Low" | "High".
confidentiality_subscoreNo"None" | "Low" | "High".

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, description fully covers behavioral traits: LLM gate on identity fields, outcomes (accepted/rejected), server-derived impact from factors, and error codes (503, 502, 400). 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?

Description is front-loaded with purpose and uses clear sections and bullet points. It is somewhat verbose but each sentence adds necessary detail. Minor redundancy in repeating 'factor fields' but overall well-structured.

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 high parameter count (16), high schema coverage (94%), presence of output schema (implied), and no annotations, the description covers all necessary context: LLM gating, rejection outcomes, audit trail requirements, and error handling. No significant gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 94%, so baseline is 3. Description adds value by explaining that impact is server-composed from factor fields and that change_reason is required with factors. It groups identity fields vs. factor/notes, aiding understanding beyond the schema.

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 'Edit an existing asset. Only provided fields changed.' It distinguishes from sibling 'restore_asset' by noting that editing a soft-deleted asset is rejected and requires restoration first. The verb-resource pair is precise.

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 advises when not to use (soft-deleted assets, use restore_asset). Also requires change_reason when factor fields are supplied. The LLM-gating conditions for identity fields are stated, giving clear context for invocation.

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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