How to Use the Landbot MCP in AutoGen

Q: How do I configure AutoGen to use this Landbot MCP Server?

Install the autogen-extmcp package. Pass your server URL to mcpservertools and feed the resulting list directly into your AssistantAgent constructor.

Deploy multi-agent support teams that debate, route, and resolve Landbot conversations using AutoGen.

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Connect Landbot MCP to AutoGen

Create your Vinkius account to connect Landbot 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 Landbot with AutoGen

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Debate customer intent before routing

The MCP Server's `get_messages` tool feeds your AutoGen agents the raw chat transcript required to negotiate a response. A sentiment agent analyzes user frustration. Meanwhile, a technical agent scans for specific product errors. They argue over the true intent before making a move. This consensus model prevents knee-jerk automated replies. The agents review the context together, challenge assumptions, and only proceed when they agree on the best path forward.

Execute consensus actions via MCP Server

The `send_text_message` tool allows the winning agent to push the agreed-upon response directly to the user. Once the debate concludes, the designated speaker agent formats the text and fires the API call. The user gets an answer vetted by multiple specialized personas. You handle complex lookups the same way. A research agent calls `search_customers` to verify an email address, then passes that ID to a support agent who pulls the full profile.

Manage human escalations automatically

Your team of agents uses `assign_agent` when they determine a problem requires human intervention. If the risk-assessment agent flags a high-value customer, it overrides the technical agent's attempt to troubleshoot and forces the handoff. They verify the system state first. The agents pull `list_bots` via the MCP connection to understand the current routing topology before pushing the user into a specific live queue.

Setup guide

Set up Landbot 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 Landbot 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="Landbot_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

Install the autogen-ext[mcp] package. Pass your server URL to mcp_server_tools and feed the resulting list directly into your AssistantAgent constructor.

They pull it directly. An agent calls the message fetching tool to grab the sequence, then shares that transcript with the rest of the multi-agent team for analysis.

The McpToolAdapter converts the schemas automatically. You assign specific operations to different agents, allowing a lookup agent to search while a communication agent sends replies.

Not if you structure the group chat correctly. You designate a single speaker agent with access to the messaging operation, ensuring the team debates internally before dispatching one final response.

Authentication is handled externally and connections are completely stateless. When your agents extract email addresses via search_customers, the transaction executes inside a secure V8 isolate that terminates immediately after returning the payload.

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