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list_agents

Retrieve all available conversational AI agents from the ElevenLabs MCP Server to identify and select appropriate agents for text-to-speech and audio processing tasks.

Instructions

List all available conversational AI agents

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The handler function for the list_agents tool. It fetches the list of conversational AI agents from the ElevenLabs API using client.conversational_ai.agents.list() and formats them into a comma-separated string of agent names and IDs, returning as TextContent. The @mcp.tool decorator also serves as the registration.
    @mcp.tool(description="List all available conversational AI agents")
    def list_agents() -> TextContent:
        """List all available conversational AI agents.
    
        Returns:
            TextContent with a formatted list of available agents
        """
        response = client.conversational_ai.agents.list()
    
        if not response.agents:
            return TextContent(type="text", text="No agents found.")
    
        agent_list = ",".join(
            f"{agent.name} (ID: {agent.agent_id})" for agent in response.agents
        )
    
        return TextContent(type="text", text=f"Available agents: {agent_list}")
Behavior2/5

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

No annotations are provided, so the description carries the full burden. It states a read operation ('List'), implying non-destructive behavior, but fails to disclose critical details like pagination, sorting, rate limits, or authentication requirements, which are essential for an agent to use it effectively.

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 a single, clear sentence with no wasted words. It is front-loaded and efficiently conveys the core purpose without redundancy or fluff, making it highly concise and well-structured.

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?

Given the tool's simplicity (0 parameters, no output schema), the description is minimally adequate. However, without annotations or output details, it lacks completeness for behavioral aspects like response format or error handling, which could aid the agent in proper invocation.

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 input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately avoids discussing parameters, aligning with the schema, which justifies a baseline score of 4 for not adding unnecessary information.

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 ('List') and the resource ('all available conversational AI agents'), making the purpose unambiguous. However, it does not differentiate from sibling tools like 'get_agent' or 'list_models', which reduces specificity.

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?

The description provides no guidance on when to use this tool versus alternatives such as 'get_agent' for detailed information or 'list_models' for other resources. It lacks context about prerequisites or exclusions, leaving usage unclear.

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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