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SekaiNoOwari77

mcp-3d-modeling-agent

blender_msfs_livery_export_textures

Export MSFS livery textures from Blender objects to PNG, TARGA, or DDS-compatible formats, choosing albedo, normal, composite, or emissive maps and saving to a specified output directory.

Instructions

Export livery textures from an object in PNG, TARGA, or for DDS conversion

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
formatNoOutput format: PNG, TARGA (default: PNG)
output_dirYesDirectory to save exported textures
object_nameYesName of the object with livery materials
texture_typesNoTypes to export: albedo, normal, composite, emissive (default: albedo)

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.4.0

TDQS

B3.1/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions export formats and an output destination in the schema, but does not disclose whether files are overwritten, whether the object must already have livery materials, what texture maps are exported, or what the tool returns. Minimal behavioral context beyond the basic action.

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?

The description is a single sentence with no filler. The core action and supported formats are front-loaded, making it easy to scan. It is concise without being uselessly terse, though a bit more workflow context would improve it.

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

Completeness2/5

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

For a file-exporting tool with no annotations and no output schema, the description should clarify prerequisites, output artifacts, and the relationship to DDS conversion. It only states the action and formats, leaving the agent to infer when in the MSFS livery pipeline this should be used and what success looks like.

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 description coverage is 100%, so the schema already documents all four parameters meaningfully. The description itself adds little parameter-level detail beyond 'from an object' aligning with object_name, and format names appearing in the format parameter. A baseline of 3 is appropriate because the schema does the heavy lifting.

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?

The description clearly states the action (export) and the resource (livery textures from an object), and names the supported output formats: PNG, TARGA, or for DDS conversion. It is distinguishable from sibling tools like blender_msfs_livery_export_uv_layout and blender_msfs_livery_convert_to_dds, though the distinction is implicit rather than explicit.

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

Usage Guidelines3/5

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

The phrase 'or for DDS conversion' implies a workflow where this tool is used before blender_msfs_livery_convert_to_dds, but it never explicitly states when to use this tool versus alternatives or when not to use it. There are no direct exclusions or sibling comparisons, so guidance remains inferred rather than stated.

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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