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gen_tripo_post_process

Submit Tripo convert_model post-process tasks to convert 3D models to desired formats, controlling face limit, scale, pivot, and orientation for downstream use.

Instructions

Submit a Tripo convert_model post-process task.

KB: see knowledge_base/31_GENERATIVE_CONTENT_PIPELINE.md#tripo-task-family Example: gen_tripo_post_process(task_id="model-task-id", target_format="FBX", confirm_spend=True)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
quadNo
task_idYes
face_limitNo
scale_factorNo
session_nameNodefault
confirm_spendNo
target_formatNoFBX
export_orientationNo+x
pivot_to_center_bottomNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

C2.7/5.0
Behavior2/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It reveals that the tool 'submits' a task, implying asynchronous behavior, and the example's confirm_spend=True hints at cost, but it does not explain credit consumption, side effects, failure modes, or whether the tool waits for completion.

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 compact and front-loaded: the core action is stated first, followed by a useful KB reference and a concrete invocation example. It contains no filler, though it could earn a 5 with slightly more structured parameter guidance.

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 9-parameter tool with no annotations and no parameter descriptions, this description is incomplete. The output schema helps with return values and the KB link provides deeper context, but an agent still lacks enough information to correctly choose and populate most parameters for a real call.

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%, so the description must compensate. The example adds meaning for task_id, target_format, and confirm_spend, but the other six parameters (quad, face_limit, scale_factor, session_name, export_orientation, pivot_to_center_bottom) are left undocumented in both schema and description.

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 clearly states the action ('Submit') and the specific resource ('a Tripo convert_model post-process task'), so an agent understands what the tool does. It does not explicitly contrast itself with related gen_tripo_* tools, but the verb+resource combination is sufficiently specific to identify this as the post-processing entry point.

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 guidance is given on when to use this tool versus alternatives like gen_tripo_refine_model, gen_tripo_texture_model, or gen_tripo_wait_for_task. The KB pointer and example hint at usage context, but there is no explicit when-to-use, prerequisite, or exclusion information.

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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