How to Use the ElevenLabs MCP in Pydantic AI

Q: What happens if the model list changes?

Your agent will receive the updated list from listmodels. Since you are using typed models, the validation layer ensures your agent handles the new data correctly.

Use Pydantic AI with ElevenLabs to get type-safe audio generation and verified API responses for your agent.

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Connect ElevenLabs MCP to Pydantic AI

Create your Vinkius account to connect ElevenLabs to Pydantic AI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup ElevenLabs with Pydantic AI

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Validated audio generation

Every response from `text_to_speech` is validated against your Pydantic schemas. If the API returns malformed data, your agent catches it immediately. This prevents runtime errors caused by unexpected fields. You get a stable, predictable interface for all your audio generation tasks.

Type-safe voice management

Use `list_voices` to get a structured list of speakers. Your agent knows exactly what to expect because the output is cast to your defined Pydantic models. This enforces consistency across your entire application. You spend less time debugging data structures and more time building your agent's logic.

Usage tracking with strict types

Call `get_subscription` to get your usage stats wrapped in a verified model. You ensure your agent never operates on corrupted usage data. Use `list_pronunciation_dictionaries` to fetch your definitions safely. Your agent will always have the correct pronunciation rules ready for the next speech task.

Setup guide

Set up ElevenLabs MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "elevenlabs-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to ElevenLabs tools.",
)

result = await agent.run("List recent ElevenLabs transactions")
print(result.output)

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Common questions about ElevenLabs MCP in Pydantic AI

Every tool response is validated against a Pydantic model at runtime. If the output doesn't match your schema, the agent will throw an error before the bad data propagates.

Yes. Each tool is defined to output specific types. When you call `get_voice` or `get_user_info`, you'll receive objects that match your expected data structure perfectly.

Because we use strict validation, any API failure or unexpected response results in a clear validation error. This makes it trivial to implement retry logic or fallbacks.

Your agent will receive the updated list from `list_models`. Since you are using typed models, the validation layer ensures your agent handles the new data correctly.

This server only processes the specific data points requested by your tools, such as subscription status or voice metadata. Your credentials remain local, and the server strictly enforces the schema for all incoming and outgoing traffic.

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