Skip to main content
Glama

post-texture-inferences

Convert image textures into multiple texture maps including height, normal, smoothness, metallic, edge, and ambient occlusion maps for 3D material creation.

Instructions

Trigger the conversion of an image texture to different texture maps:

  • Height map

  • Normal map

  • Smoothness map

  • Metallic map

  • Edge map

  • Ambient Occlusion map

The process will create a new Asset with the above texture maps as children + the original image as an Albedo map.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
originalAssetsNoIf set to true, returns the original asset without transformation
dryRunNo
defaultParametersNoIf true, use the default parameters
polishedNoHow polished is the surface? 0 is like a rough surface, 1 is like a mirror
angularNoHow angular is the surface? 0 is like a sphere, 1 is like a mechanical object
invertNoTo invert the relief
saveFlipbookNoSave a flipbook of the texture. Deactivated when the input texture is larger than 2048x2048px
textureYesThe asset to convert in texture maps. Must reference an existing AssetId.
raisedNoHow raised is the surface? 0 is flat like water, 1 is like a very rough rock
shinyNoHow shiny is the surface? 0 is like a matte surface, 1 is like a diamond
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses the creation of a new Asset with children, which is useful behavioral context. However, it lacks details on permissions, rate limits, error handling, or the processing time. The description doesn't contradict annotations, but it's incomplete for a mutation tool with no structured safety hints.

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?

The description is front-loaded with the core purpose in the first sentence, followed by a bulleted list of outputs and a concise explanation of the result. Every sentence earns its place with no redundant information, making it efficient and well-structured for quick understanding.

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?

Given the complexity (10 parameters, mutation operation) and lack of annotations or output schema, the description is moderately complete. It covers the purpose and output structure but misses critical details like error conditions, performance expectations, or deeper parameter interactions. It's adequate as a minimum viable description but has clear gaps for a tool with many parameters.

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 description coverage is high (90%), so the baseline is 3. The description adds value by clarifying the output structure (new Asset with texture maps as children + original as Albedo map), which isn't covered in the input schema. It doesn't detail individual parameters, but the high schema coverage and added output context justify a score above 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?

The description clearly states the specific action ('Trigger the conversion') and resource ('an image texture'), listing the exact texture maps produced (Height, Normal, Smoothness, Metallic, Edge, Ambient Occlusion) and the outcome (new Asset with children). It distinguishes from siblings like 'post-caption-inferences' or 'post-remove-background-inferences' by focusing on texture map generation from an image.

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?

No explicit guidance on when to use this tool versus alternatives is provided. While the description implies it's for converting image textures to maps, it doesn't mention prerequisites (e.g., input texture requirements) or compare to similar tools like 'post-controlnet-texture-inferences' or 'post-img2img-texture-inferences' from the sibling list, leaving usage context unclear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/pasie15/scenario.com-mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server