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brandon-fryslie

elevenlabs-mcp

list_agents

Read-only

List all available conversational AI agents in your ElevenLabs account for easy management and selection.

Instructions

List all available conversational AI agents

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYes
textYes
annotationsNo
_metaNo

Implementation Reference

  • MCP tool registration for list_agents using FastMCP's @tool decorator with read-only hint and description
    @mcp.tool(
        annotations=ToolAnnotations(readOnlyHint=True, openWorldHint=True),
        description="List all available conversational AI agents"
    )
  • Handler function that calls ElevenLabs conversational_ai.agents.list() API and formats the agent names/IDs into a comma-separated list, returning a TextContent response
    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}")
Behavior3/5

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

Annotations already provide readOnlyHint and openWorldHint, indicating safe read-only operation. The description adds 'available' but does not contradict annotations; no further behavioral detail needed.

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?

A single, front-loaded sentence with zero wasted words. Perfectly concise for the simplicity of the tool.

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

Completeness5/5

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

Given no parameters, annotations covering safety, and likely an output schema for results, the description is complete. It covers the essential 'list all' behavior.

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?

No parameters exist, so schema coverage is 100% and baseline is 4. The description does not need to add parameter information.

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 verb 'List' and the resource 'all available conversational AI agents', distinguishing it from sibling tools like get_agent (single) or create_agent (creation).

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?

The description implies this is for listing all agents, but does not explicitly state when to use alternatives like get_agent for a specific agent. However, given the tool's simplicity, the context is clear.

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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