How to Use the BotPenguin MCP in CrewAI

Q: Can a CrewAI team monitor active BotPenguin chats?

Yes. A specialized triage agent can run listchats on a schedule to discover new conversations, then delegate the response task to a writer agent.

Q: How do CrewAI agents send messages through BotPenguin?

Your agents invoke the sendmessage tool during their execution phase. They format the response based on the conversation history and post it back to the customer.

Deploy autonomous agent teams in CrewAI to manage your BotPenguin support queues and contacts.

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Connect BotPenguin MCP to CrewAI

Create your Vinkius account to connect BotPenguin 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 BotPenguin with CrewAI

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Autonomous queue monitoring with CrewAI

The `list_chats` tool pulls all active customer threads directly into your CrewAI shared memory space. A dedicated monitor agent uses this list to detect incoming messages that need immediate attention. You assign this task to a triage agent who scans the queue. Once identified, a responder agent takes over to draft replies, ensuring a smooth handoff between specialized team members.

Lead lookup and verification pipelines

Searching your lead database is handled by `list_contacts`, which accepts optional search strings to locate specific users. A research agent uses this tool to gather context before writing email or chat replies. By exposing this tool through the CrewAI MCP Server integration, your agents access live customer profiles autonomously. They run sequential tasks to verify email addresses and update tags without human intervention.

Multi-agent messaging and OTP verification

Sending live responses to users is managed by `send_message`, which routes agent-generated text directly back to the active chat. When security is required, the team uses `send_otp` to verify the customer's mobile number. You configure this by passing your Vinkius endpoint URL directly into the agent's mcps parameter. For advanced setups, use MCPServerHTTP to filter which tools your agents can access.

Setup guide

Set up BotPenguin 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 BotPenguin tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

task = Task(
    description="List recent BotPenguin 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 →

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="BotPenguin Analyst",
        goal="Access and analyze BotPenguin data via MCP.",
        backstory="Expert analyst with direct BotPenguin access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent BotPenguin 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 BotPenguin. 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 BotPenguin MCP in CrewAI

Add your Vinkius endpoint URL to the mcps list when initializing your CrewAI agent. The framework automatically registers all customer support tools for use during tasks.

Yes. A specialized triage agent can run `list_chats` on a schedule to discover new conversations, then delegate the response task to a writer agent.

Your agents invoke the `send_message` tool during their execution phase. They format the response based on the conversation history and post it back to the customer.

Yes, you can configure the MCP connection using MCPServerHTTP from the crewai.mcp package. This lets you apply a tool filter to restrict agents to specific actions, like only reading contacts.

Vinkius handles every MCP execution in memory using isolated V8 sandboxes. Your chat messages and operator lists are never cached or stored on disk, maintaining strict compliance with your data policies.

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