How to Use the MoEngage MCP in AutoGen

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Connect MoEngage MCP to AutoGen

Create your Vinkius account to connect MoEngage 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 MoEngage with AutoGen

Ask AI about this MCP

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Perplexity

Debate campaign strategy with AutoGen agents

Let your agents use `list_campaigns` to review current activity and argue about the best next steps. One agent proposes a change while another reviews the stats from `get_campaign_stats`. This leads to a consensus-based decision on your next move. You watch the agents negotiate in real-time until they agree on the action.

Update device info via AutoGen agents

Your agents call `update_device` as part of a multi-step workflow to keep user profiles accurate. They can challenge each other on whether an update is necessary based on the current `get_customer` output. This ensures your agents only perform updates when they agree it's valid. It prevents junk data from hitting your production environment.

Execute bulk tracking tasks in AutoGen

Your agents use `track_events_bulk` to report findings or user actions back to your system. They coordinate the timing of these updates to ensure your data stays consistent. This keeps your tracking logic under the control of your agent team. They handle the batching to maintain performance throughout the conversation.

Setup guide

Set up MoEngage 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 MoEngage 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="MoEngage_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

Get your connection token →

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Common questions about MoEngage MCP in AutoGen

You provide the tool list to the AssistantAgent constructor. All agents in your group chat then have access to the MoEngage functions for their discussions.

Yes, agents call multiple tools in sequence, using the output of one as the input for the next. This allows for complex logic like checking a segment before sending a push.

You configure the API key in your environment variables. The MCP server uses these to authenticate all tool calls initiated by your agents.

You define constraints in your agent system prompt. This guides the agents on when to call a tool versus when to discuss the strategy further.

Your customer data is never stored by the server. It passes through the MCP protocol directly to your local agent environment, ensuring no intermediate persistence.

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