How to Use the meetergo MCP in CrewAI

Deploy autonomous CrewAI agents to manage your entire scheduling pipeline using the meetergo MCP server.

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Connect meetergo MCP to CrewAI

Create your Vinkius account to connect meetergo 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 meetergo with CrewAI

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Specialized agents for CrewAI scheduling

Assign a researcher agent to `get_meeting_type` and a moderator agent to `create_booking`. The crew collaborates to match leads with the correct sales rep. This modular setup keeps your code organized. Each agent only sees the tools it needs to perform its specific role in the pipeline.

Autonomous booking monitoring

Set a monitor agent to watch your calendar via `list_bookings`. If it spots a gap or a conflict, it triggers the necessary updates to keep your schedule clean. It runs unattended in your background processes. You get a fully automated ops team that manages your calendar without human intervention.

Direct integration with CrewAI

Connect your agents to the server by passing the endpoint directly to your agent configuration. It supports multiple transport types to fit your infrastructure needs. Setup takes seconds. Once connected, your agents treat the tools like native functions and start executing tasks immediately.

Setup guide

Set up meetergo 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 meetergo tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

You can. Use a tool filter in your MCP configuration to ensure each agent only has access to the specific tools it needs, like `get_availability`.

CrewAI uses shared memory between agents. When one agent creates a booking, the others can see the updated status through the shared memory state.

The server uses a zero-trust model. Access to your booking and user data is restricted to the agents you explicitly authorize with your endpoint token.

You can run the server via stdio or SSE. It works perfectly with local development environments before you push to production.

The agents are programmed to handle tool errors as part of the task flow. If a booking fails, the agent reports the status back to your central monitor.

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