How to Use the AgentMail MCP in AutoGen
Equip your AutoGen multi-agent squads with real email inboxes to negotiate, draft, and send messages.
Works with every AI agent you already use
…and any MCP-compatible client
Connect AgentMail MCP to AutoGen
Create your Vinkius account to connect AgentMail 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.
Attach the AgentMail MCP Server to AutoGen squads
Multi-agent systems need a way to communicate with the outside world. You hook up the `agentmail-mcp` tools, and your primary communication agent runs `create_inbox` to provision a custom address immediately. The rest of the squad uses that address to interact with human users. A support agent monitors incoming traffic using `list_threads`, pulling fresh complaints into the group chat for the other agents to analyze.
Debate email drafts before sending
You do not want a single LLM firing off raw emails to your clients. A drafting agent writes a proposed response and shares it in the AutoGen conversation. A secondary compliance agent reviews the text, suggests edits, and forces a rewrite. They argue until they reach a consensus. Once the compliance agent approves the final copy, the execution agent triggers `reply_to_message` to send the sanitized email. You get the safety of peer review without human intervention.
Triage and route complex attachments
Incoming files often require different specialists. When a message arrives with a payload, a triage agent runs `get_attachment` to download the base64 data. It decodes the file and looks at the contents. If the file is a legal contract, the triage agent tags the legal analysis agent. If it is a technical spec, it wakes up the engineering agent. They process the document and collectively decide whether to `forward_message` to a specific human department.
Set up AgentMail 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 includesmcp_server_toolsfor 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
AssistantAgentand callagent.run(). The agent invokes AgentMail tools and returns structured results.
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="AgentMail_assistant",
model_client=OpenAIChatCompletionClient(model="gpt-4o"),
tools=tools,
)
result = await agent.run("List recent AgentMail data")
print(result.messages[-1].content) 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.
McpWorkbenchis 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=workbenchto your agent. State is preserved across multiple tool calls within the same session.
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="AgentMail_assistant",
model_client=OpenAIChatCompletionClient(model="gpt-4o"),
workbench=workbench,
)
result = await agent.run("List recent AgentMail data")
print(result.messages[-1].content) Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by AgentMail. 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 AgentMail MCP in AutoGen
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