How to Use the Vagaro MCP in CrewAI

Q: How do I automate reporting on Vagaro appointments using CrewAI?

You can assign a 'Reporting Agent' to run listappointments for a specific date. This agent compiles the list, and another specialized agent formats it into a clean summary report.

Q: What data does Vagaro touch when using CrewAI?

The server touches staff schedules (getstaffschedule), client records (contact info, visit history), service details, and product stock levels. The multi-agent system handles all these domain types.

Q: How can I get service pricing details in Vagaro using CrewAI?

Use the listservices tool. A dedicated agent pulls this information, including pricing and duration, allowing another specialized agent to immediately calculate total package costs for a client.

Run autonomous Vagaro operations with CrewAI. Specialized agents collaborate on complex business tasks.

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Connect Vagaro MCP to CrewAI

Create your Vinkius account to connect Vagaro 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 Vagaro with CrewAI

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Coordinate Staff Scheduling via MCP Server

You don't just check availability; you assign roles. A 'Scheduling Agent' can use `get_staff_schedule` to see all booked slots, while a 'Manager Agent' uses `list_staff` to verify specialties and roles. The agents work together: one gathers the raw data, and another analyzes it against business rules (e.g., checking if the staff member has the required specialty for a given service).

Process Client Data with CrewAI

A 'Client Research Agent' uses `search_clients` to gather history and contact info, while an 'Analytics Agent' reviews that data. This provides shared memory across the process. This structured approach allows you to build monitoring processes—one agent watches for critical updates, another takes action on them.

Manage Product Lifecycle using CrewAI

Use a 'Inventory Agent' that runs `list_products` and checks stock levels. It then passes this data to an 'Ordering Agent,' which compares the low stock count against established reorder thresholds. The system executes these steps sequentially, ensuring that inventory status dictates the next action taken by the crew.

Setup guide

Set up Vagaro 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 Vagaro tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

You can assign a 'Reporting Agent' to run `list_appointments` for a specific date. This agent compiles the list, and another specialized agent formats it into a clean summary report.

The server touches staff schedules (`get_staff_schedule`), client records (contact info, visit history), service details, and product stock levels. The multi-agent system handles all these domain types.

Yes. You can create specialized agents for different functions—one focusing on retail (`list_products`) and another focusing on services (`list_services`). They collaborate to manage the whole picture.

For complex operations, yeah. Instead of one agent trying to do everything (which often fails), you assign specific roles—a 'Moderator Agent' oversees the whole session while others handle specific tasks like checking `get_client` data.

Use the `list_services` tool. A dedicated agent pulls this information, including pricing and duration, allowing another specialized agent to immediately calculate total package costs for a client.

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