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Mipiti
by Mipiti

split_composition_entity

Split an entity from an ancestor to multiple descendants, duplicating its state and soft-deleting the original. Use when an entity is descendant-specific and should exist per descendant.

Instructions

Push an ancestor-owned entity down to one or more descendants and soft-delete the ancestor's copy. Mutates state across the ancestor + every target descendant.

Inverse of lift_composition_entity. Use when an entity that currently lives on an ancestor is in fact descendant-specific and should be modeled separately per descendant — the operator chooses which descendants take a copy. A new local id is minted on each target; attached state on the ancestor's entity (assertions, jira mappings, risk acceptances, etc.) is duplicated to every target.

The route's model_id IS the ancestor (the entity being split lives on it). Each affected model (ancestor + every target descendant) bumps version and emits a model_refined activity event; a structured split_applied event with the full split_event payload lands on the ancestor. The audit pack surfaces this under split_history.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kindYesEntity kind — one of ``"assets"``, ``"attackers"``, ``"components"``.
model_idYesAncestor model id — the entity to split lives here.
server_versionYes
ancestor_local_idYesLocal id of the entity on the ancestor.
target_descendantsYesNon-empty list of descendant model ids that should each take a copy.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

No annotations are provided, so the description carries full burden. It discloses mutations, soft-delete, state duplication, version bumps, and emitted events. However, it does not mention auth or rate limits.

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?

Four sentences with clear progression: action, usage, details, effects. No wasted words; each sentence earns its place.

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 presence of an output schema, the description covers the full operation, side effects, and events comprehensively. It is complete for a mutation tool.

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 80% (4 of 5 parameters documented). The description adds context about model_id being the ancestor and target_descendants being non-empty, but server_version remains unexplained. With high schema coverage, baseline 3 is appropriate.

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 uses a specific verb ('push down', 'soft-delete') and resource ('ancestor-owned entity') and clearly distinguishes from sibling 'lift_composition_entity'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states when to use ('when entity is descendant-specific') and names the inverse tool. It does not provide explicit exclusions but context is clear.

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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