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bake_skeletal_mesh_lod

Destructive

Regenerate skeletal mesh LODs in Unreal Engine by running the editor reduction pipeline, with options for target LOD count and base LOD generation.

Instructions

Run the editor's skeletal-mesh reduction pipeline via USkeletalMeshEditorSubsystem::RegenerateLOD.

Returns: { skeletal_mesh_path: string, old_lod_count: int, new_lod_count: int, lod_count: int, baked: bool, regenerate_even_if_imported: bool, generate_base_lod: bool, bake_warning: string }

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
new_lod_countNoTarget LOD count; <= 0 keeps current and re-runs reduction
generate_base_lodNobool, re-derive LOD 0 from source (default false)
skeletal_mesh_pathYesContent path to the skeletal mesh asset
regenerate_even_if_importedNobool, regenerate even when LODs are imported (default false)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.1.0

TDQS

B3.2/5.0
Behavior3/5

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

Annotations already declare destructiveHint=true, readOnlyHint=false, and idempotentHint=false, so the safety profile is covered. The description adds little behavioral context beyond naming the underlying subsystem call; it doesn't say whether the asset is saved, what gets overwritten, or that reduction destroys existing LODs.

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?

Two tight parts: a one-line purpose statement followed by the return shape. It is front-loaded and wastes no words, and the Returns list is genuinely useful given there is no output schema.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, documenting the return fields is a real contribution, and the annotations carry the destructive/idempotency profile. However, for a destructive bake operation it omits whether the change persists or requires saving, and gives no usage context, leaving the definition only adequate.

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 100%, so the schema already documents all four parameters, including the <=0 semantics for new_lod_count and the defaults for the booleans. The description adds no parameter meaning beyond the schema, so the baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource: it runs the skeletal-mesh reduction pipeline via RegenerateLOD, which clearly distinguishes it from getters like get_skeletal_mesh_lod_info and setters like set_skeletal_mesh_lod_reduction_settings. It does not explicitly differentiate itself from siblings such as add_skeletal_mesh_lod or remove_skeletal_mesh_lod, so it stops short of a 5.

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

Usage Guidelines2/5

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

The description gives no when-to-use guidance, no prerequisites, and no routing to alternatives. With siblings like add_skeletal_mesh_lod, set_skeletal_mesh_lod_reduction_settings, and set_skeletal_mesh_lod_settings in the same family, an agent gets no help choosing this tool over them.

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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