Tripo MCP Server

Instructions

Create a 3D model from a text description using the Tripo API.

IMPORTANT: This tool initiates a 3D model generation task but does NOT wait for completion.
After calling this tool, you MUST repeatedly call the get_task_status tool with the returned
task_id until the task status is SUCCESS or a terminal error state.

Typical workflow:
1. Call create_3d_model_from_text to start the task
2. Get the task_id from the response
3. Call get_task_status with the task_id
4. If status is not SUCCESS, wait a moment and call get_task_status again
5. Repeat until status is SUCCESS or a terminal error state
6. When status is SUCCESS, use the pbr_model_url from the response

Args:
    describe_the_look_of_object: A detailed description of the object to generate.
    face_limit: The maximum number of faces in the model.
    auto_size: Whether to automatically size the model.

Returns:
    A dictionary containing the task ID and instructions for checking the status.

Implementation Reference

• src/server.py:718-780 (handler)

  The handler function for 'create_3d_model_from_text' tool. Decorated with @mcp.tool() which handles both registration and schema generation from the function signature and docstring. Initiates a Tripo API text-to-3D model generation task asynchronously and returns the task_id for subsequent status checks.

  @mcp.tool()
  async def create_3d_model_from_text(
      describe_the_look_of_object: str, face_limit: int = -1
  ) -> Dict[str, Any]:
      """
      Create a 3D model from a text description using the Tripo API.
  
      IMPORTANT: This tool initiates a 3D model generation task but does NOT wait for completion.
      After calling this tool, you MUST repeatedly call the get_task_status tool with the returned
      task_id until the task status is SUCCESS or a terminal error state.
  
      Typical workflow:
      1. Call create_3d_model_from_text to start the task
      2. Get the task_id from the response
      3. Call get_task_status with the task_id
      4. If status is not SUCCESS, wait a moment and call get_task_status again
      5. Repeat until status is SUCCESS or a terminal error state
      6. When status is SUCCESS, use the pbr_model_url from the response
  
      Args:
          describe_the_look_of_object: A detailed description of the object to generate.
          face_limit: The maximum number of faces in the model.
          auto_size: Whether to automatically size the model.
  
      Returns:
          A dictionary containing the task ID and instructions for checking the status.
      """
      # Get the API key from environment variable
      # api_key = os.environ.get("TRIPO_API_KEY")
      get_blender_connection()
      blender = get_blender_connection()
      api_key = _tripo_apikey
      if not api_key:
          raise ValueError(
              "It may take some more time to correctly re-enter the tripo function. You can first check the scene and then try the tripo function again later"
          )
  
      # Create the Tripo client
      async with TripoClient(api_key=api_key) as client:
          # Create a text-to-model task
          task_id = await client.text_to_model(
              prompt=describe_the_look_of_object,
              face_limit=face_limit,
          )
  
          # Get initial task status
          task = await client.get_task(task_id)
  
          # Return immediately with task ID and status
          return {
              "task_id": task_id,
              "status": str(task.status),
              "progress": task.progress,
              "message": "Task created successfully. The 3D model generation is in progress.",
              "next_step": "You MUST now call get_task_status with this task_id to check progress.",
              "important_note": "3D model generation takes 3-5 minutes. You need to repeatedly call get_task_status until completion.",
              "workflow": [
                  "1. You've completed this step by calling create_3d_model_from_text",
                  "2. Now call get_task_status with task_id: " + task_id,
                  "3. If status is not SUCCESS, wait and call get_task_status again",
                  "4. When status is SUCCESS, use the pbr_model_url from the response",
              ],
          }

• src/server.py:718-718 (registration)

  The @mcp.tool() decorator registers the function as an MCP tool, automatically generating input schema from parameters and output from return type/docstring.

  @mcp.tool()

• src/server.py:719-721 (schema)

  Function signature defining the input schema: describe_the_look_of_object (str, required), face_limit (int, optional default -1). Output is Dict[str, Any].

  async def create_3d_model_from_text(
      describe_the_look_of_object: str, face_limit: int = -1
  ) -> Dict[str, Any]: