How to Use the Landbot MCP in CrewAI

Q: How does a CrewAI team collaborate on Landbot tasks?

Agents in a crew share context. One agent can use listcustomers to find a user, and the next agent in the sequence automatically gets that customer's ID to use with getmessages or assignagent. This MCP server gives them the tools to act on that shared knowledge.

Q: Can a CrewAI agent read a customer's entire chat history?

It can. The getmessages tool fetches the conversation for a given customer. A 'Reviewer Agent' could use this to analyze sentiment or look for keywords before deciding whether to escalate with assignagent.

Deploy autonomous agent crews to manage Landbot conversations and escalations with CrewAI. True hands-off operation.

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Connect Landbot MCP to CrewAI

Create your Vinkius account to connect Landbot to CrewAI 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 CrewAI

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Autonomous Triage Crew

Assign a crew of agents to manage your support queue 24/7. A 'Scout Agent' can constantly run `list_customers` and `get_messages` to find new conversations or identify ones that have been waiting too long for a reply. Once the Scout finds a priority case, it passes the customer ID to a 'Dispatcher Agent'. The Dispatcher's only job is to execute the `assign_agent` tool, routing the conversation to the right human team. This division of labor is what makes CrewAI so effective.

Proactive Bot Monitoring Crew

You can deploy a crew to keep an eye on your chatbot fleet. An 'Auditor Agent' can run on a schedule, using `list_bots` to get a full inventory. It then passes each bot's ID to a 'Detailer Agent'. The Detailer agent uses `get_bot` to fetch the specific configuration for each bot. If it finds a misconfiguration or an inactive bot that should be active, it can report back to the Auditor, which then triggers an external alert. This whole process runs without any human input.

Automated Customer Re-engagement

Use a specialized CrewAI team to win back customers. For example, a 'Finder Agent' could be triggered by an external event (like a canceled subscription) and use `search_customers` to locate that user in Landbot. Once found, it hands off to a 'Writer Agent' that drafts a personalized follow-up. Finally, a 'Sender Agent' takes the text and uses `send_text_message` to deliver it. Each agent has one job, and the crew ensures it gets done.

Setup guide

Set up Landbot MCP in CrewAI

Prerequisites

Python 3.10+ installed
crewai package (pip install crewai)
Active Vinkius subscription with a valid endpoint token

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke Landbot tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="Landbot Analyst",
    goal="Access and analyze Landbot data via MCP.",
    backstory="Expert analyst with direct Landbot access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent Landbot transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

server_params = SseServerParameters(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="Landbot Analyst",
        goal="Access and analyze Landbot data via MCP.",
        backstory="Expert analyst with direct Landbot access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Landbot transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

    crew = Crew(agents=[agent], tasks=[task])
    result = crew.kickoff()
    print(result)

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 CrewAI

Agents in a crew share context. One agent can use `list_customers` to find a user, and the next agent in the sequence automatically gets that customer's ID to use with `get_messages` or `assign_agent`. This MCP server gives them the tools to act on that shared knowledge.

Yes. When you define your agent in CrewAI, you can provide a specific list of tools. You could give a 'Scout Agent' read-only tools like `list_customers`, while a 'Dispatcher Agent' only gets access to `assign_agent`.

For a quick start, just pass your Vinkius MCP endpoint URL directly into the `mcps` list when you create your Agent. CrewAI will automatically fetch the tools and make them available.

It can. The `get_messages` tool fetches the conversation for a given customer. A 'Reviewer Agent' could use this to analyze sentiment or look for keywords before deciding whether to escalate with `assign_agent`.

The crew accesses Landbot customer data (names, emails) and message content through a single, secure MCP endpoint. Even though multiple agents are running, every tool call is an independent, ephemeral operation. Nothing is stored or shared outside the crew's immediate memory.

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