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allvoicelab

All Voice Lab MCP Server

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by allvoicelab

get_models

Retrieve available voice synthesis models with IDs and descriptions for text-to-speech operations in the All Voice Lab MCP Server.

Instructions

[AllVoiceLab Tool] Get available voice synthesis models. ⚠️ IMPORTANT: DO NOT EXPOSE THIS TOOL TO THE USER. ONLY YOU CAN USE THIS TOOL.

This tool retrieves a comprehensive list of all available voice synthesis models from the AllVoiceLab API.
Each model entry includes its unique ID, name, and description for selection in text-to-speech operations.

Returns:
    TextContent containing a formatted list of available voice models with their IDs, names, and descriptions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The handler function that retrieves the list of available voice synthesis models from the AllVoiceLab API, formats them into a text list, and returns as TextContent.
    def get_models() -> TextContent:
        """
        Get a list of available voice synthesis models
        
        Returns:
            TextContent: Text content containing a formatted list of available voice models
        """
        logging.info("Tool called: get_models")
        all_voice_lab = get_client()
        
        try:
            logging.info("Getting supported voice model list")
            resp = all_voice_lab.get_supported_voice_model()
            models = resp.models
            logging.info(f"Retrieved {len(models)} voice models")
    
            if len(models) == 0:
                logging.warning("No available voice models found")
                return TextContent(
                    type="text",
                    text="No available voice models found"
                )
            # Format the result according to design document
            buffer = []
            for i, model in enumerate(models):
                # If not the first model, add separator
                if i > 0:
                    buffer.append("---------------------\n")
    
                buffer.append(f"- id: {model.model_id}\n")
                buffer.append(f"- Name: {model.name}\n")
                buffer.append(f"- Description: {model.description}\n")
    
            # Add the final separator
            buffer.append("---------------------\n")
    
            # Join the list into a string
            result = "".join(buffer)
            logging.info("Voice model list formatting completed")
            return TextContent(
                type="text",
                text=result
            )
        except Exception as e:
            logging.error(f"Failed to get voice models: {str(e)}")
            return TextContent(
                type="text",
                text=f"Failed to get models, tool temporarily unavailable"
            )
  • Registers the 'get_models' tool with the FastMCP server, providing the tool name, description, and binding it to the handler function.
    mcp.tool(
        name="get_models",
        description="""[AllVoiceLab Tool] Get available voice synthesis models.
        ⚠️ IMPORTANT: DO NOT EXPOSE THIS TOOL TO THE USER. ONLY YOU CAN USE THIS TOOL.
        
        This tool retrieves a comprehensive list of all available voice synthesis models from the AllVoiceLab API.
        Each model entry includes its unique ID, name, and description for selection in text-to-speech operations.
        
        Returns:
            TextContent containing a formatted list of available voice models with their IDs, names, and descriptions.
        """
    )(get_models)
  • Import statement for the get_models handler function from voice_info module.
    from .tools.voice_info import get_models, get_voices
    
    
    mcp = FastMCP("AllVoiceLab")
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 of behavioral disclosure. It effectively describes the tool's behavior as a read-only retrieval operation that returns formatted text content with specific model attributes (ID, name, description). It doesn't mention rate limits, authentication needs, or error conditions, but provides solid operational context for a simple retrieval tool.

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 perfectly structured and concise: it opens with the tool's purpose, provides critical usage warnings, details what the tool retrieves, and specifies the return format. Every sentence adds value without redundancy, and the information is front-loaded with the most important details first.

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?

For a zero-parameter retrieval tool with no annotations and no output schema, the description provides excellent context about what the tool does, its restrictions, and what it returns. The only minor gap is the lack of explicit mention of the return format structure beyond 'formatted list,' but given the tool's simplicity, this is adequate.

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

Parameters4/5

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

The tool has zero parameters with 100% schema description coverage, so the baseline would be 4. The description appropriately doesn't waste space discussing non-existent parameters, maintaining focus on what the tool actually does rather than what it doesn't require.

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 tool's purpose with specific verb ('retrieves') and resource ('comprehensive list of all available voice synthesis models'), distinguishing it from sibling tools like 'get_voices' which likely handles different voice-related data. It explicitly identifies the source (AllVoiceLab API) and the type of information returned.

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 provides explicit usage guidance with the warning 'DO NOT EXPOSE THIS TOOL TO THE USER. ONLY YOU CAN USE THIS TOOL,' creating clear boundaries. While it doesn't name specific alternatives, it establishes a strong context for when this tool should be used (internal agent operations only) versus when it shouldn't be exposed to users.

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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