How to Use the Mav MCP in AutoGen

Q: Can AutoGen agents manage multiple Mav playbooks?

They can. By calling listplaybooks, the agents get a full view of available options and select the best one for each specific lead.

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Connect Mav MCP to AutoGen

Create your Vinkius account to connect Mav 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.

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Setup Mav with AutoGen

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Debate lead qualification in AutoGen

Assign one agent to monitor for new leads while another reviews the `get_lead` output. They debate whether the lead is ready for a high-touch playbook. If they agree, the system calls `create_lead` to start the process. This prevents your agents from spamming low-quality contacts.

Manage SMS playbooks with AutoGen agents

Your agents compare current performance metrics from `list_activities` against your goals. They decide if a lead needs a different playbook and use `stop_playbook` if necessary. This creates a self-correcting sales loop. The agents adjust their strategy based on the real-time feedback they get from the Mav API.

Negotiate lead status in AutoGen

Agents challenge each other when a lead wants to opt-out. One agent tries to re-engage while another respects the `opt_out_lead` request to preserve brand reputation. This ensures your SMS outreach remains professional. The agents reach a consensus before taking any action that affects your customer relationships.

Setup guide

Set up Mav 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 Mav 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="Mav_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Mav 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="Mav_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

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

Get your connection token →

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Common questions about Mav MCP in AutoGen

You pass the tool definitions to your AssistantAgent. The agents then negotiate which tool to call based on the conversation context.

They can. By calling `list_playbooks`, the agents get a full view of available options and select the best one for each specific lead.

The server provides the tools for the agents to act once they finish their discussion. The actual API calls happen only after the agents reach a decision.

All tool calls are isolated. Only the agents you explicitly authorize have access to the lead and playbook records.

Yes, any opt-out event is logged and can be used by the agents to adjust their future outreach strategy. It ensures the agents act on the latest lead preferences.

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