How to Use the Vagaro MCP in AutoGen

Q: If I need product details, how does Vagaro work with AutoGen?

You can set up an agent debate: one asks about stock via listproducts, and another argues the best retail placement. The agents resolve conflicts to suggest a final display strategy.

Drive consensus on salon operations using AutoGen and the Vagaro MCP Server.

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Connect Vagaro MCP to AutoGen

Create your Vinkius account to connect Vagaro to AutoGen 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 AutoGen

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Resolve Service Conflicts with MCP Server

Set up a debate between two agents. One agent calls `list_services` to get all offerings, while another calls `get_business_info` for pricing structure. They then negotiate the final price or service grouping based on competing inputs.

Automate Staffing Decisions

You can run a multi-agent debate around scheduling. One agent checks staff availability using `get_staff_schedule`. A second agent, simulating management policy, then challenges that schedule against the capacity limits provided by `list_classes`, forcing a consensus on staffing changes.

Validate Client Status and Services

A debate can validate client needs. Agent One calls `get_client` to pull preferences, while Agent Two calls `list_services` for the best match. They must argue until they converge on a single recommended service package.

Setup guide

Set up Vagaro MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes Vagaro tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

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

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="Vagaro_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Vagaro data")
print(result.messages[-1].content)

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Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

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

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="Vagaro_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Vagaro data")
    print(result.messages[-1].content)

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.

Why Choose Vinkius

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Common questions about Vagaro MCP in AutoGen

You deploy multiple agents to debate optimal schedules. One agent checks `get_staff_schedule` and another reviews class capacity via `list_classes`, forcing the system to find a consensus time slot.

Yes. The framework is designed for this. You pass multiple tools, like `get_client` and `search_clients`, allowing different agents to call them sequentially to reach a shared conclusion.

You can set up an agent debate: one asks about stock via `list_products`, and another argues the best retail placement. The agents resolve conflicts to suggest a final display strategy.

The system can debate which services are most profitable by comparing data from `list_services` and the overall business profile (`get_business_info`). It finds the best mix.

The server touches Client Profile and Appointment Data. When running multi-agent conversations, ensure all agents respect PII boundaries during their deliberation phase.

Use it with your favorite AI tools

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