How to Use the MailSlurp MCP in AutoGen

Q: How do I load MailSlurp tools into AutoGen?

Run pip install -U "autogen-extmcp". Use mcpservertools with your MCP Server parameters, and pass the resulting list into your AssistantAgent constructor.

Q: Can AutoGen agents read the actual contents of a MailSlurp email?

Yes. Once an inbox receives a message, the agent calls getemaildetails. This returns the full body and headers for the agents to analyze.

Q: How do agents handle delays in email delivery?

Instead of writing custom retry loops, agents use waitforlatestemail. The MCP Server handles the polling and returns the message as soon as it drops into the inbox.

Q: Can multiple AutoGen agents share the same MailSlurp instance?

Yes. The McpToolAdapter converts the schemas automatically, allowing any agent in your conversation group to call tools like listinboxemails when needed.

Give your AutoGen multi-agent teams the tools to provision, test, and debate MailSlurp email workflows.

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Perplexity

Multi-agent testing via the MCP Server

Testing complex authentication flows requires checking multiple conditions. A QA agent triggers a password reset, while a monitoring agent uses `wait_for_latest_email` to catch the inbound message. They debate the results. If the email is missing headers or the link is malformed, the agents flag the failure and log the exact output from `get_email_details`.

Coordinate inbox lifecycles

Resource management works better when agents police each other. One agent executes `create_random_inbox` to start a load testing sequence. A separate cleanup agent monitors the active resources using `list_all_inboxes`. Once the first agent finishes, the cleanup agent steps in and fires `delete_specific_inbox` to prevent bloat.

Simulate user communication

You can build a realistic simulation where agents email each other. An agent adopts a persona and uses `send_email_from_inbox` to dispatch a message to another agent's temporary address. The receiving agent relies on `wait_for_email_count` to know when the batch is complete. They read the contents, deliberate on the response, and continue the chain.

Setup guide

Set up MailSlurp 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 MailSlurp 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="MailSlurp_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

Run `pip install -U "autogen-ext[mcp]"`. Use `mcp_server_tools` with your MCP Server parameters, and pass the resulting list into your `AssistantAgent` constructor.

Yes. Once an inbox receives a message, the agent calls `get_email_details`. This returns the full body and headers for the agents to analyze.

Instead of writing custom retry loops, agents use `wait_for_latest_email`. The MCP Server handles the polling and returns the message as soon as it drops into the inbox.

Yes. The `McpToolAdapter` converts the schemas automatically, allowing any agent in your conversation group to call tools like `list_inbox_emails` when needed.

The system handles raw HTML bodies and one-time passcodes securely. Vinkius isolates the MCP Server execution environment, ensuring that sensitive email payloads disappear the moment the connection terminates.

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