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SekaiNoOwari77

mcp-3d-modeling-agent

blender_ai_generate_texture_sync

Generate PBR textures from a text prompt, wait for the result, and receive file paths with optional auto-apply to Blender objects.

Instructions

Generate PBR texture and wait for completion (synchronous). Returns texture file paths when done.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
seedNoRandom seed for reproducible results
promptYesText description of the desired texture (e.g., 'worn red brick wall', 'brushed steel')
timeoutNoMaximum wait time in seconds (default: 300)
auto_applyNoAutomatically apply generated textures to the object's material
resolutionNoTexture resolution in pixels
object_nameNoName of the Blender object to apply the texture to
negative_promptNoWhat to avoid in generation (default: 'blurry, low quality, watermark, text, logo')

Schema Changelog

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

  1. First observedv0.4.0

TDQS

A3.7/5.0
Behavior2/5

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

No annotations are provided, so the description bears the full burden of behavioral disclosure. It discloses blocking/waiting and the return value, but it omits the important default side effect that auto_apply=true will modify the target object's material, and it says nothing about timeout or failure behavior. This is a significant transparency gap for a generation tool with no annotations.

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?

Two short sentences deliver the core information: the operation, the synchronous behavior, and the return value. There is no filler, repetition, or schema duplication.

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?

For a 7-parameter tool with no output schema and no annotations, this description is adequate but thin. It communicates the main blocking behavior and return value, and the schema covers parameter documentation, but it does not mention the default auto-apply mutation, timeout behavior, or failure modes. This makes it workable but incomplete for fully informed invocation.

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 baseline applies; all seven parameters including defaults and enums are already documented in the schema. The description adds no additional parameter-level meaning beyond framing the output as PBR texture file paths.

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 states a specific verb and resource: generate a PBR texture, wait synchronously, and return texture file paths. This clearly conveys what the tool does and distinguishes it from asynchronous or non-PBR texture generation siblings.

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?

The description makes the intended usage context clear: use this tool when you need a blocking synchronous generation call that returns paths when done. It does not explicitly name an async alternative or state when not to use it, but the synchronous framing is a clear contextual signal.

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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