How to Use the Chaport MCP in AutoGen

Q: How do I initialize the Chaport MCP Server in AutoGen?

Install the autogen-extmcp package and use mcpservertools with your Vinkius URL. The framework automatically converts the tool schemas for your AssistantAgent.

Q: Does AutoGen support streaming Chaport tool calls?

It handles this natively. You configure StreamableHttpServerParams to maintain the connection while your agents debate the next action.

Build AutoGen agent swarms that debate customer support replies before sending them via Chaport.

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Connect Chaport MCP to AutoGen

Create your Vinkius account to connect Chaport 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 Chaport with AutoGen

Ask AI about this MCP

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Claude

Perplexity

Debate Chaport MCP Server responses

Customer escalations rarely have obvious answers. You can build a swarm where a technical agent reviews the problem while a tone-checking agent ensures the reply sounds empathetic. They pass the output of `get_visitor_last_chat` back and forth until they agree. Once consensus is reached, the designated execution agent takes over. It fires `send_agent_message` to push the approved text directly to the waiting website visitor.

Monitor visitor behavior collaboratively

A dedicated analytics agent can sit in the background calling `list_website_visitors` at regular intervals. When it spots a high-value prospect lingering on a pricing page, it alerts a sales agent. The sales agent then runs `get_visitor_details` to gather context. It might even ask a third agent to verify if the user has an existing account before initiating a proactive chat.

Negotiate operator workloads

Routing tickets effectively requires understanding who is busy. Your supervisor agent checks `list_online_agents` and `list_chaport_operators` to get a snapshot of the current floor. If the queue is heavy, agents deliberate on whether to handle the query autonomously or wait for a human. They look at `get_my_agent_profile` to confirm routing permissions before making the final call.

Setup guide

Set up Chaport 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 Chaport 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="Chaport_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

Install the `autogen-ext[mcp]` package and use `mcp_server_tools` with your Vinkius URL. The framework automatically converts the tool schemas for your AssistantAgent.

Yes. You can distribute the tools across different agents in your swarm. One agent might handle reading chat histories while another holds the exclusive right to send messages.

Complex customer support requires deliberation. A single script just executes, but an AutoGen swarm allows a security agent to veto a proposed reply if it detects sensitive information.

It handles this natively. You configure `StreamableHttpServerParams` to maintain the connection while your agents debate the next action.

Vinkius manages the connection via a strictly ephemeral architecture. Live chat transcripts and operator metadata are processed in isolated memory spaces that vanish the second your agent swarm terminates the session.

Use it with your favorite AI tools

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