How to Use the Bot9 MCP in CrewAI

Q: How do I pass Bot9 tools to a CrewAI agent?

Install the tools with pip install crewai "crewaitools". You can then pass your hosted MCP server URL directly into your Agent definition using the mcps array parameter.

Q: How do CrewAI agents collaborate on Bot9 support chats?

One agent can pull active threads via listconversations, while a specialized moderator agent reads the history with getconversationhistory and drafts replies. They collaborate autonomously using shared memory.

Coordinate teams of CrewAI agents to train, monitor, and converse through Bot9 autonomously.

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Connect Bot9 MCP to CrewAI

Create your Vinkius account to connect Bot9 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 Bot9 with CrewAI

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Multi-Agent Bot9 Support Operations with CrewAI

Set up a dedicated CrewAI support team where one agent monitors Bot9 chats using `list_conversations` and another analyzes logs with `get_conversation_history`. A third CrewAI agent can then craft and send replies using `send_message` to the active Bot9 conversation. CrewAI's shared memory allows these specialized agents to pass Bot9 context back and forth. Your CrewAI monitor agent spots a stuck Bot9 customer, and your responder agent gets the full history before sending an answer.

Autonomous Bot9 Training Teams

Deploy a CrewAI researcher agent that finds outdated documentation and a trainer agent that updates Bot9. The researcher compiles new links, and the trainer uses `add_data_source` to feed them into the Bot9 knowledge base. Confirming the Bot9 update succeeded is easy when the trainer agent executes `list_data_sources`. This entire loop runs autonomously, keeping your customer-facing Bot9 assistants smart without CrewAI developers lifting a finger.

Fleet Management via MCP Server

Let a CrewAI supervisor agent oversee your entire collection of Bot9 customer service bots. The supervisor uses `list_bots` to track active Bot9 systems and `create_bot` to deploy new specialized assistants when support volume spikes. Running this Bot9 MCP Server within CrewAI lets you restrict tool exposure across your agent fleet. You can configure the supervisor CrewAI agent to have full access while limiting junior agents to basic Bot9 message sending tools.

Setup guide

Set up Bot9 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 Bot9 tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="Bot9 Analyst",
    goal="Access and analyze Bot9 data via MCP.",
    backstory="Expert analyst with direct Bot9 access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent Bot9 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="Bot9 Analyst",
        goal="Access and analyze Bot9 data via MCP.",
        backstory="Expert analyst with direct Bot9 access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Bot9 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 Bot9. 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 Bot9 MCP in CrewAI

Install the tools with `pip install crewai "crewai[tools]"`. You can then pass your hosted MCP server URL directly into your Agent definition using the `mcps` array parameter.

Yes, use the MCP server class from the CrewAI library and pass a `tool_filter`. This lets you restrict a junior agent to `send_message` while keeping `create_bot` exclusive to your admin agent.

One agent can pull active threads via `list_conversations`, while a specialized moderator agent reads the history with `get_conversation_history` and drafts replies. They collaborate autonomously using shared memory.

Yes, you can connect CrewAI to your hosted Bot9 instance using stdio, SSE, or Streamable HTTP. The framework handles the transport negotiation automatically under the hood.

The integration touches sensitive conversational text, chatbot configurations, and source URLs. Vinkius runs a zero-trust model where every single tool call is sandboxed and isolated, ensuring your support logs never leak between agents or external networks.

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