How to Use the Vibrato MCP in AutoGen

Q: How does Vibrato help AutoGen agents debate call actions?

The MCP Server exposes tools like createcall and listcalls. The agents treat these as functions they must agree on. They debate the optimal sequence of calls to achieve a final, validated action.

Q: If I debate using templates with Vibrato and AutoGen, what's the outcome?

The agents will use listtemplates to review options, then select one for execution via createcallfromtemplate. The final result is a negotiated plan of action.

Q: Can I see all available templates in AutoGen?

Yeah, you use the listtemplates tool call. This output gives the agent's knowledge base everything it needs to discuss and decide which template is best for the user.

Get consensus decisions on calls using AutoGen and the Vibrato MCP Server.

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Perplexity

Debating complex call creation strategies

Need to figure out the best way to start a call? You can let multiple agents debate which tool to use. One agent might suggest `create_call` based on a prompt, while another checks if a template is better by using `list_templates`. The final decision comes from consensus, not just the first idea thrown out.

Validating call outcomes through debate

Agents can challenge each other's assumptions about completed calls. One agent might suggest checking `get_call_status` because the previous result was ambiguous. Another agent will cross-reference this by calling `list_calls` to confirm the record exists. This multi-agent process provides robust verification that a simple single-step tool call can't match.

Negotiating the best template execution

When you need a scripted conversation, agents debate which flow to use. They first run `list_templates` to see all options available. Then they might choose to execute one via `create_call_from_template`, simulating the negotiation process. This makes the system accountable for its choices.

Setup guide

Set up Vibrato 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 includes mcp_server_tools for 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 AssistantAgent and call agent.run(). The agent invokes Vibrato tools and returns structured results.

agent.py

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="Vibrato_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Vibrato data")
print(result.messages[-1].content)

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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. McpWorkbench is 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=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

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="Vibrato_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Vibrato data")
    print(result.messages[-1].content)

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Vibrato. 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 Vibrato MCP in AutoGen

The MCP Server exposes tools like `create_call` and `list_calls`. The agents treat these as functions they must agree on. They debate the optimal sequence of calls to achieve a final, validated action.

The system simulates deliberation. If two agents disagree on whether a call is complete, they both check `get_call_status` until consensus is reached on the final status code.

The agents will use `list_templates` to review options, then select one for execution via `create_call_from_template`. The final result is a negotiated plan of action.

Yeah, you use the `list_templates` tool call. This output gives the agent's knowledge base everything it needs to discuss and decide which template is best for the user.

The server touches communication metadata, including phone numbers and call status. All interactions are handled within Vinkius's isolated sandbox, keeping your data secure during multi-agent discussion.

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