How to Use the Sender.net MCP in AutoGen

Q: How do I load Sender.net tools into AutoGen?

Install the autogen-extmcp package. Fetch the tools using mcpservertools with your HTTP parameters, then pass that list directly to your AssistantAgent.

Q: Can multiple AutoGen agents use the Sender.net MCP Server simultaneously?

Yes. You can equip a specialized analytics agent with getcampaignanalytics while giving a separate marketing agent access to createcampaign.

Q: Does the AutoGen integration support Sender.net subscriber filtering?

The agents can call listsubscribers and pass group or status filters. They parse the JSON response to inform their next conversational turn.

Q: How does AutoGen handle errors from the Sender.net tools?

If getuserprofile fails, the agent sees the error output. It can then discuss the failure with a debugging agent or retry the tool call autonomously.

Let your AutoGen agents debate and deploy Sender.net marketing campaigns autonomously via this MCP Server.

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Setup Sender.net with AutoGen

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AutoGen MCP Server Coordination

One agent drafts copy. Another agent reviews it for spam triggers. They debate the subject line. Once they agree, the deployment agent fires `create_campaign`. You build systems where decisions require consensus. The analytics agent pulls `get_campaign_analytics` and argues that the last send failed. The drafting agent adjusts its tone accordingly.

Autonomous List Management

Bad data ruins deliverability. Your compliance agent monitors bounces and unsubscribes. It actively prunes the database using `delete_subscriber`. Meanwhile, a growth agent finds new leads and calls `create_subscriber`. They coordinate via `list_subscriber_groups` to ensure nobody gets dropped into the wrong segment.

Real-Time Profile Updates

Customer data changes constantly. A support agent fields a ticket and realizes a user changed their email. It immediately triggers `update_subscriber`. The agent verifies the change worked by running `get_subscriber_details`. No human intervention required. The agents discuss the state change and log the result in their conversation history.

Setup guide

Set up Sender.net 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 Sender.net 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="Sender.net_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

    result = await agent.run("List recent Sender.net 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 Sender.net. 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 Sender.net MCP in AutoGen

Install the `autogen-ext[mcp]` package. Fetch the tools using `mcp_server_tools` with your HTTP parameters, then pass that list directly to your `AssistantAgent`.

Yes. You can equip a specialized analytics agent with `get_campaign_analytics` while giving a separate marketing agent access to `create_campaign`.

The agents can call `list_subscribers` and pass group or status filters. They parse the JSON response to inform their next conversational turn.

If `get_user_profile` fails, the agent sees the error output. It can then discuss the failure with a debugging agent or retry the tool call autonomously.

These tools process personally identifiable information like names and email addresses. Vinkius secures the transport layer using an ephemeral sandbox. Your agents only hold the data in memory during their conversation.

Use it with your favorite AI tools

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