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allvoicelab

All Voice Lab MCP Server

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by allvoicelab

get_dubbing_info

Check the status and progress of a video dubbing task by providing its unique identifier. This tool returns current processing stage and completion details for previously submitted dubbing jobs.

Instructions

[AllVoiceLab Tool] Retrieve status and details of a video dubbing task.

This tool queries the current status of a previously submitted dubbing task and returns detailed information
about its progress, including the current processing stage and completion status.

Args:
    dubbing_id: The unique identifier of the dubbing task to check. This ID is returned from the video_dubbing or video_translation_dubbing tool. Required.
    
Returns:
    TextContent containing the status (e.g., "pending", "processing", "success", "failed") and other details of the dubbing task.
    
Limitations:
    - The dubbing_id must be valid and properly formatted
    - The task must have been previously submitted to the AllVoiceLab API

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dubbing_idYes

Implementation Reference

  • The handler function that implements the core logic of the 'get_dubbing_info' tool. It validates input, calls the AllVoiceLab client API, formats the response, and returns it as TextContent.
    def get_dubbing_info(dubbing_id: str) -> TextContent:
        """
        Retrieve status and details of a video dubbing task
        
        Args:
            dubbing_id: The unique identifier of the dubbing task to check. This ID is returned from the video_dubbing or video_translation_dubbing tool. Required.
            
        Returns:
            TextContent: Text content containing the status (e.g., "pending", "processing", "success", "failed") and other details of the dubbing task.
        """
        all_voice_lab = get_client()
        logging.info(f"Tool called: get_dubbing_info")
        logging.info(f"Dubbing ID: {dubbing_id}")
    
        # Validate parameters
        if not dubbing_id:
            logging.warning("Dubbing ID parameter is empty")
            return TextContent(
                type="text",
                text="dubbing_id parameter cannot be empty"
            )
    
        try:
            logging.info("Getting dubbing task information")
            dubbing_info = all_voice_lab.get_dubbing_info(dubbing_id)
            logging.info(f"Dubbing info retrieved successfully for ID: {dubbing_id}")
    
            # Format the result
            buffer = []
            buffer.append(f"Dubbing ID: {dubbing_info.dubbing_id}\n")
            buffer.append(f"Status: {dubbing_info.status}\n")
    
            if dubbing_info.name:
                buffer.append(f"Project Name: {dubbing_info.name}\n")
            buffer.append(
                "Note: If the task has not been completed, you may need to explicitly inform the user of the task ID when responding.\n")
    
            # Join the list into a string
            result = "".join(buffer)
            return TextContent(
                type="text",
                text=result
            )
        except Exception as e:
            logging.error(f"Failed to get dubbing information: {str(e)}")
            return TextContent(
                type="text",
                text=f"Failed to get dubbing information, tool temporarily unavailable"
            )
  • The MCP tool registration for 'get_dubbing_info', including name, description (which serves as input/output schema), and binding to the handler function.
    mcp.tool(
        name="get_dubbing_info",
        description="""[AllVoiceLab Tool] Retrieve status and details of a video dubbing task.
        
        This tool queries the current status of a previously submitted dubbing task and returns detailed information
        about its progress, including the current processing stage and completion status.
        
        Args:
            dubbing_id: The unique identifier of the dubbing task to check. This ID is returned from the video_dubbing or video_translation_dubbing tool. Required.
            
        Returns:
            TextContent containing the status (e.g., "pending", "processing", "success", "failed") and other details of the dubbing task.
            
        Limitations:
            - The dubbing_id must be valid and properly formatted
            - The task must have been previously submitted to the AllVoiceLab API
        """
    )(get_dubbing_info)
Behavior4/5

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

With no annotations provided, the description carries the full burden and does well by disclosing key behavioral traits: it's a read-only query (implied by 'retrieve' and 'queries'), requires a valid previously submitted task, and returns status details. It mentions the API context ('AllVoiceLab API') but doesn't cover rate limits or authentication needs, leaving some gaps.

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 appropriately sized and front-loaded with the core purpose in the first sentence. The structured sections (Args, Returns, Limitations) are clear, though the 'Limitations' section could be more concise by integrating constraints into the parameter explanation. Every sentence adds value, with minimal redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (single parameter, no output schema, no annotations), the description is largely complete: it covers purpose, usage, parameters, returns, and limitations. However, it lacks details on error handling or specific return formats beyond status examples, which could be helpful for an agent. The absence of an output schema means the description should ideally elaborate more on return values.

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

Parameters5/5

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

The schema has 0% description coverage (no parameter descriptions in schema), so the description fully compensates by explaining the 'dubbing_id' parameter: its purpose ('unique identifier'), source ('returned from video_dubbing or video_translation_dubbing'), and constraints ('must be valid and properly formatted'). This adds essential meaning beyond the bare 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?

The description clearly states the specific action ('Retrieve status and details') and resource ('video dubbing task'), distinguishing it from siblings like 'download_dubbing_audio' (which fetches output) or 'get_removal_info' (which checks subtitle removal). It explicitly mentions the tool is for checking previously submitted tasks, not initiating new ones.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states when to use this tool: for checking the status of a 'previously submitted dubbing task' where the ID comes from 'video_dubbing or video_translation_dubbing'. It also specifies limitations that imply when not to use it (e.g., invalid IDs or unsupported tasks), providing clear alternatives and prerequisites.

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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