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Gatyh

3D Texel MCP server

by Gatyh
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TypeScript
Hybrid

AI PBR Material Generator

generate_pbr_material
Idempotent

Creates tileable PBR textures from a text prompt, outputting albedo, normal, roughness, and height maps. Returns a generation ID to poll for results.

Instructions

Seamless PBR material from a text prompt: albedo (base color), normal, roughness and height maps. Realistic or stylized. Precise layouts (herringbone, chevron, Versailles parquet, basket weave, hexagon tiles...) are drawn exactly so they tile. Asynchronous: returns a generation id; poll get_generation. Spends credits (refunded automatically on failure). Typical time: 120 s.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNotexel-quality = best (2K/4K/4K native), texel-express = fastest (2K/4K), texel-classic = alternative look.texel-quality
styleNorealistic
promptYesMaterial description, e.g. "mossy medieval cobblestone, wet".
resolutionNo4k
max_creditsYesHighest price the user accepted (from quote_generation). The call fails with confirmation_required if missing.
output_formatNopng
idempotency_keyNoOptional. Same key = same generation, never charged twice. Default: derived from the parameters for 10 minutes; pass a new value to intentionally run the same prompt again.
save_to_libraryNoOptional. When the generation succeeds, publish it in the public 3DTexel community library and add it to the user's library (free). Only if the user asked for it.
enhance_seamlessNoBeta: second AI pass that repaints the edges (+5 to +10 credits, about 1 minute longer).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.1/5.0
Behavior4/5

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

Annotations provide readOnlyHint=false, idempotentHint=true, destructiveHint=false. Description adds valuable context beyond annotations: asynchronous operation, credit cost and automatic refunds, typical duration, and precise tiling behavior. Does not contradict annotations.

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?

Front-loads core purpose, outputs, and key behavioral traits. Efficient, with no redundant sentences. Slightly dense but all information earns its place.

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

Completeness4/5

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

For a complex async generation tool with 9 parameters and no output schema, description covers asynchronous nature, credit refunds, timing, and outputs. Missing explicit mention of output format or resolution but schema handles those. Adequate but could mention the idempotency and save_to_library behavior more explicitly.

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 67% (6 of 9 params have descriptions). Description names output maps but adds no parameter-specific meaning beyond schema. Baseline 3 is appropriate since schema covers most parameters.

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 (generates) and resource (PBR material from text prompt) with outputs listed (albedo, normal, roughness, height maps). Distinguishes from sibling generate_texture by specifying PBR maps and precise tiling layouts, and from generate_hdri by material focus.

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

Usage Guidelines4/5

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

Provides clear usage context: asynchronous workflow (poll get_generation), credit spending (refunded on failure), typical time (120s). However, no explicit when-to-use vs siblings like generate_texture or generate_hdri is given; user must infer from output types.

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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