Skip to main content
Glama

tripo_import

Imports local GLB, FBX, OBJ, or STL models (up to 150MB) into Studio as a project so you can texture, remesh, rig, or animate them.

Instructions

把本地模型(GLB / FBX / OBJ / STL,≤150MB)导入 Studio 变成一个项目(对应官方 import_model),之后可以对它用 tripo_texture / tripo_remesh / tripo_rig / tripo_animate 等。内部接口 operation/import_user_model。

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "model_path": "<model_path>"
    },
    "name": "tripo_import"
  }
}

Input Schema

NameRequiredDescriptionDefault
nameNo项目名,默认文件名
model_pathYes本地模型文件
wait_secondsNo默认 480 秒
use_original_uvNo保留原有 UV,默认 true
transform_matrixNo4×4 变换矩阵(16 个数,列主序),默认单位矩阵;用来把模型转到正面朝前

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.7.0

TDQS

A3.8/5.0
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 it does add real context: the size ceiling, accepted formats, and the fact that it creates a persistent project. It is silent on blocking vs. async behavior (the wait_seconds parameter hints at a synchronous wait but this is never explained), on failure handling, and on what identifiers come back.

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?

A single dense sentence that front-loads the action and the accepted formats, with the sibling tools named last. Efficient, though the two internal-endpoint identifiers are marginally more detail than an agent needs to select the tool.

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 five-parameter mutation with no annotations and no output schema, the description covers inputs well but not outcomes — it never says what the tool returns (a project id? a status?) or whether the call waits. An agent can select it correctly but must guess at the response contract.

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 all five parameters are already documented, and the baseline applies. The description adds only the format and 150MB constraint tied to model_path; it says nothing extra about name, wait_seconds, use_original_uv, or transform_matrix beyond the schema.

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 (导入/import) and resource (本地模型 → Studio 项目), plus supported formats (GLB/FBX/OBJ/STL) and a size cap (≤150MB). The parenthetical mapping to the official `import_model` and the internal `operation/import_user_model` endpoint make it unmistakable versus sibling generators like tripo_generate_text.

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?

Makes clear this is the entry point for an existing local file and names the follow-on tools (tripo_texture / tripo_remesh / tripo_rig / tripo_animate), which implicitly tells an agent when to pick it. It does not, however, explicitly exclude the alternative of generating a model from text/image with the sibling tools, leaving that inference to the reader.

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