How to Use the ElevenLabs MCP in AutoGen
Let your AutoGen agents debate and manage ElevenLabs voice assets autonomously.
Works with every AI agent you already use
…and any MCP-compatible client
Connect ElevenLabs MCP to AutoGen
Create your Vinkius account to connect ElevenLabs to AutoGen and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.
Negotiate audio production via MCP
AutoGen agents do not just execute commands blindly. By connecting this MCP Server, you can assign one agent to write a script and another to critique it. Once they agree on the final draft, a dedicated production agent calls `text_to_speech` to generate the actual file. The debate extends to voice selection. A creative agent might suggest a specific persona from `list_voices`, while a quality assurance agent checks `list_models` to ensure the newest neural engine is used. They negotiate the parameters before spending a single character of your quota.
Enforce budgets across AutoGen swarms
Running autonomous agents can drain your API credits if left unchecked. You can build a financial oversight agent that specifically monitors `get_subscription_info` and `get_account_info`. It tracks character usage across the entire swarm. If the swarm approaches its limit, the oversight agent intervenes. It can block other agents from calling generation tools or force them to reuse existing files via `list_audio_history` and `get_download_link`. You get autonomous cost control built directly into the conversation flow.
Maintain strict voice governance
Temporary voice clones pile up fast when multiple agents experiment with audio via MCP tools. A designated cleanup agent can review the current workspace using `get_voice` and `get_voice_settings`. It identifies test clones that are no longer actively used. When it finds an abandoned test clone, the agent runs `delete_voice` to remove the clutter. It also scrubs sensitive test recordings by calling `delete_history_item`. Your AutoGen system maintains a clean, compliant environment without you ever logging into a developer dashboard.
Set up ElevenLabs MCP in AutoGen
Prerequisites
- Python 3.10+ installed
-
autogen-ext[mcp]package - Active Vinkius subscription with a valid endpoint token
- 1
Install AutoGen with MCP
Run
pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includesmcp_server_toolsfor stateless tool access. - 2
Fetch tools from the MCP
Call
mcp_server_tools(SseServerParams(url=...))with your Vinkius endpoint. Replace[YOUR_TOKEN_HERE]with your token from cloud.vinkius.com. - 3
Run your agent
Pass the tools to
AssistantAgentand callagent.run(). The agent invokes ElevenLabs tools and returns structured results.
from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
server_params = SseServerParams(
url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
tools = await mcp_server_tools(server_params)
agent = AssistantAgent(
name="ElevenLabs_assistant",
model_client=OpenAIChatCompletionClient(model="gpt-4o"),
tools=tools,
)
result = await agent.run("List recent ElevenLabs data")
print(result.messages[-1].content) Prerequisites
- Python 3.10+ installed
-
autogen-ext[mcp]+autogen-agentchat - Active Vinkius subscription with a valid endpoint token
- 1
Install dependencies
Same packages as above.
McpWorkbenchis ideal when your agent needs stateful sessions across multiple tool calls. - 2
Use McpWorkbench as context manager
Wrap your agent in
async with McpWorkbench(...)to maintain shared state and resources. The workbench manages the full MCP session lifecycle. - 3
Run with workbench
Pass
workbench=workbenchto your agent. State is preserved across multiple tool calls within the same session.
from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
server_params = SseServerParams(
url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
async with McpWorkbench(server_params) as workbench:
agent = AssistantAgent(
name="ElevenLabs_assistant",
model_client=OpenAIChatCompletionClient(model="gpt-4o"),
workbench=workbench,
)
result = await agent.run("List recent ElevenLabs data")
print(result.messages[-1].content) Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by ElevenLabs. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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Common questions about ElevenLabs MCP in AutoGen
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