How to Use the Appier MCP in AutoGen

Q: How do I connect Appier to my AutoGen agents?

Install autogen-extmcp and configure StreamableHttpServerParams with your Vinkius URL. Pass the resulting tool list to your AssistantAgent constructor.

Give your AutoGen agents the ability to debate and analyze Appier MCP campaigns using real-time data.

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Connect Appier MCP to AutoGen

Create your Vinkius account to connect Appier 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 Appier with AutoGen

Ask AI about this MCP

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Claude

Perplexity

Multi-Agent Campaign Analysis

The `get_campaign` and `get_campaign_analytics` tools give your AutoGen setup hard numbers to argue about. A performance agent pulls the metrics while a budget agent reviews the spend. They discuss the results and challenge each other's conclusions. The system will not output a recommendation until both agents reach a consensus based on the actual marketing data.

Negotiate Appier Audience Segments

Calling `list_audiences` and `list_segments` feeds demographic data directly into the chat. Your strategy agents review the configured groups and debate which segment offers the best return on investment. This setup mimics a real marketing team. One agent might push to target a broader audience, while another uses the tool output to argue for a narrower, high-converting segment.

Connect MCP Servers to AutoGen

Tools like `list_predictions` and `list_conversions` allow your agents to compare expected outcomes against actual results. The MCP adapter converts the Appier schema automatically so the agents understand the payload. You build systems where decisions require deliberation. The agents pull the prediction models, argue over the tracking data, and finally agree on the next steps for your marketing strategy.

Setup guide

Set up Appier 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 Appier 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="Appier_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

Get your connection token →

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

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

Get your connection token →

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

Install autogen-ext[mcp] and configure StreamableHttpServerParams with your Vinkius URL. Pass the resulting tool list to your AssistantAgent constructor.

Yes. Any agent configured with the tool list can execute queries. They will share the returned campaign data in the group chat.

No. The framework handles schema conversion automatically. Your agents read the raw JSON and parse the marketing metrics natively.

Yes. You can use Streamable HTTP transports to keep the connection open while your agents deliberate over the campaign data.

Your campaign analytics and segment configurations are handled via ephemeral MCP execution. The Vinkius managed infrastructure authenticates the request, fetches the data, and immediately terminates the sandbox.

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