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remove_skeletal_mesh_lod

Destructive

Delete a non-zero LOD from a skeletal mesh asset, shift higher LODs down, then save the asset to persist the change.

Instructions

Remove one LOD from a skeletal mesh asset, with its source model and cached reduction data; higher LODs shift down one index. lod_index must be 1 or more and below the LOD count, otherwise an error (LOD 0 is never removed). Marks the mesh dirty without saving or rebuilding render data, so follow with save_asset. Use add_skeletal_mesh_lod to append a LOD, get_skeletal_mesh_lod_info to inspect them, set_skeletal_mesh_min_lod to skip the most detailed LODs while keeping them.

Returns: { skeletal_mesh_path: string, lod_index: int, lod_count: int, removed: bool }

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
lod_indexYesIndex of the LOD to remove (must be > 0)
skeletal_mesh_pathYesContent path to the skeletal mesh asset

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.1.0

TDQS

A4.9/5.0
Behavior5/5

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

Annotations only signal destructive=true; the description adds what is actually destroyed, the index-shift cascade, the invalid-index error condition, and the crucial fact that the mesh is marked dirty without saving or rebuilding render data. This is well beyond what the annotations disclose.

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?

Front-loaded with the core action and its side effect, then constraints, then alternatives, then return shape. Dense but every clause carries decision-relevant information with no filler.

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?

With no output schema, the description supplies the return shape, and it covers the preconditions, side effects, and required follow-up that an agent needs to invoke this destructive mutation correctly.

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 100% and already states lod_index 'must be > 0', but the description adds the upper bound (below the LOD count), the error behavior, and the LOD 0 never-removed rule. Adds genuine value over the schema baseline.

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?

States a specific verb and resource ('Remove one LOD from a skeletal mesh asset') with scope detail (source model and cached reduction data are removed, higher LODs shift down). Clearly distinguishable from siblings like add_skeletal_mesh_lod and remove_skeletal_mesh_socket.

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 names alternatives and their selection conditions: add_skeletal_mesh_lod to append, get_skeletal_mesh_lod_info to inspect, set_skeletal_mesh_min_lod to skip rather than remove, and calls out the required save_asset follow-up. Nothing is left to inference.

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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