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get_agent

Retrieve detailed information about a specific conversational AI agent by providing its unique identifier.

Instructions

Get details about a specific conversational AI agent

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
agent_idYes

Implementation Reference

  • The main handler function for the 'get_agent' tool, decorated with @mcp.tool for registration. It retrieves details of a specific agent using the ElevenLabs client API, extracts voice configuration if available, and returns a formatted TextContent response.
    @mcp.tool(description="Get details about a specific conversational AI agent")
    def get_agent(agent_id: str) -> TextContent:
        """Get details about a specific conversational AI agent.
    
        Args:
            agent_id: The ID of the agent to retrieve
    
        Returns:
            TextContent with detailed information about the agent
        """
        response = client.conversational_ai.agents.get(agent_id=agent_id)
    
        voice_info = "None"
        if response.conversation_config.tts:
            voice_info = f"Voice ID: {response.conversation_config.tts.voice_id}"
    
        return TextContent(
            type="text",
            text=f"Agent Details: Name: {response.name}, Agent ID: {response.agent_id}, Voice Configuration: {voice_info}, Created At: {datetime.fromtimestamp(response.metadata.created_at_unix_secs).strftime('%Y-%m-%d %H:%M:%S')}",
        )
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 of behavioral disclosure. It states it 'gets details' which implies a read-only operation, but doesn't specify if it requires authentication, rate limits, error conditions, or what format/details are returned. This is a significant gap for a tool with no annotation coverage.

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, efficient sentence that directly states the tool's purpose without unnecessary words. It's front-loaded and every part earns its place, 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.

Completeness2/5

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

Given the lack of annotations and output schema, the description is incomplete. It doesn't explain what 'details' are returned, potential errors, or behavioral traits. For a tool that retrieves specific agent information, more context is needed to guide effective use.

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

Parameters3/5

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

The description adds no parameter semantics beyond what the input schema provides (a single required 'agent_id' parameter). With 0% schema description coverage, the description doesn't compensate by explaining what 'agent_id' represents or its format. However, since there's only one parameter, the baseline is 4, but the lack of any parameter context reduces it to 3.

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 verb ('Get details') and resource ('about a specific conversational AI agent'), making the purpose understandable. It distinguishes from siblings like 'list_agents' by focusing on a single agent rather than listing multiple. However, it doesn't specify what details are included, keeping it from a perfect score.

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 like 'list_agents' or 'get_conversation'. It mentions 'specific' agent but doesn't clarify prerequisites or contexts, leaving the agent to infer usage based on the name alone.

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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