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SekaiNoOwari77

mcp-3d-modeling-agent

blender_msfs_livery_convert_to_dds

Convert MSFS livery textures to DDS format, selecting albedo, normal, composite, or emissive type for the right compression.

Instructions

Convert texture to DDS format for MSFS (requires texconv)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
input_pathYesPath to input image
output_pathNoPath for output DDS (optional)
texture_typeNoType for format selection: 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

C2.9/5.0
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 the texconv dependency, which is useful, but it does not disclose whether the tool overwrites files, what DDS format/compression is chosen for each texture_type, whether it creates directories, or what errors occur with missing texconv. For a file conversion tool with side effects, this is a significant gap.

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 concise sentence with the core action and a critical prerequisite in parentheses. It is front-loaded with the main purpose, and every word earns its place. It could be slightly more structured, but for its length it is efficient.

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?

There is no output schema, no annotations, and the tool is one of many MSFS livery and export tools. The description lacks prerequisite context (texconv installation path), behavioral detail (file overwrite, compression mapping), and any guidance on how this relates to the livery export pipeline. For a tool with three parameters and side effects, this is incomplete.

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 the three parameters. The description adds the context that texture_type values map to format selection, which is marginally helpful, but it repeats the parameter names without adding format syntax, file extension behavior, or default path logic. 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?

The description states a specific verb ('Convert') and resource ('texture to DDS format for MSFS'), which clearly distinguishes it as a format conversion tool within the MSFS livery toolset. It doesn't name sibling alternatives, but the MSFS-prefixed siblings are mostly about livery setup, materials, and exports, so the conversion purpose is reasonably distinct.

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 explicit guidance on when to use this tool versus alternatives like blender_msfs_livery_export_textures or blender_material_add_texture. The parenthetical '(requires texconv)' implies an external dependency but does not explain when conversion is necessary or what inputs are appropriate. Usage context is left entirely 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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