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gen_prepare_texture_paint_session

Plan a Tripo texture-paint edit session using a text prompt, view angle, and brush settings—without making a paid request. Set up asset names and output folders for Unreal Engine.

Instructions

Plan a Tripo texture-paint edit session without making a paid request.

KB: see knowledge_base/31_GENERATIVE_CONTENT_PIPELINE.md#d9-chat-dock-integration Example: gen_prepare_texture_paint_session(model_task_id="task_123", texture_prompt="weathered brass", view_angle="front", save_asset_name="MI_BrassPass")

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
blend_modeNosoft blend
view_angleNocurrent viewport/front
paint_notesNo
session_nameNodefault
model_task_idYes
output_folderNo/Game/Generated
brush_strengthNo
texture_promptYes
save_asset_nameNoMI_GeneratedPaintedTexture
texture_reference_imageNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A3.5/5.0
Behavior3/5

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

With no annotations, the description carries the full burden and does contribute the important behavioral fact that no paid request is made and that this is a planning operation, not a mutation. Still, it does not disclose potential side effects, whether a session or asset is created, or any permissions/credit preconditions.

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 compact and well structured: one sentence states the purpose and cost behavior, followed by a KB pointer and a concrete invocation example. There is no filler and no repetition of schema field names.

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 relatively complex 10-parameter tool with zero per-parameter schema descriptions and no annotations, the description is too thin. An agent cannot reliably determine valid values or interactions for the undocumented parameters, and while an output schema may define return shape, input semantics remain under-specified.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% for 10 parameters, and the description only compensates with a single example that illustrates model_task_id, texture_prompt, view_angle, and save_asset_name. The other six parameters (e.g., blend_mode, brush_strength, output_folder, texture_reference_image) have no semantic explanation at all.

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 uses a specific verb and resource: 'Plan a Tripo texture-paint edit session' and immediately adds the key qualifier 'without making a paid request.' This distinguishes it from actual generation/execution tools like gen_tripo_texture_model or recording tools like gen_record_texture_paint_pass without needing to open their schemas.

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 'without making a paid request' wording implies this is a preflight/planning step before a paid texture operation, which is useful context. However, it never names alternative tools or states explicit when-to-use/when-not-to-use conditions, leaving routing mostly 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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