How to Use the Acuity Scheduling MCP in AutoGen
Assemble a team of AutoGen agents to debate, plan, and manage your Acuity Scheduling calendar with conversational AI.
Works with every AI agent you already use
…and any MCP-compatible client
Connect Acuity Scheduling MCP to AutoGen
Create your Vinkius account to connect Acuity Scheduling 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.
Let agents debate the best appointment time
Design a multi-agent system for smarter scheduling. One agent, the "Scheduler," can use `list_available_times` to propose a few open slots. A second agent, the "Policy Enforcer," can then check those proposals against your business rules, like avoiding back-to-back new client meetings. This conversational approach catches issues a single agent might miss. The agents discuss the options, one pushing for availability and the other for business logic. They arrive at a consensus on the best time to book, all before any action is taken.
Collaborate on business reporting
Have your agents work together to build reports. A "Data-Puller" agent can be tasked with using `list_appointments` to get all the bookings for the last month. It then passes that raw data to an "Analyst" agent. The Analyst agent can then cross-reference appointment types against the `list_calendars` output to see which services are driving the most revenue per provider. It's a powerful way to use AutoGen's conversational structure to get more than just raw data from this MCP Server.
Build a consensus-driven product agent
Create a team of agents to manage your public offerings. One agent can use `list_classes` to monitor enrollment. If a class is almost full, it can propose creating a new one to a "Finance" agent. The Finance agent then uses `list_appointment_types` to check the price and profitability of that class type. They can debate whether it's the right time to add another session. This lets you model complex business decisions with your AutoGen agents and the Acuity MCP tools.
Set up Acuity Scheduling 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 includesmcp_server_toolsfor 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
AssistantAgentand callagent.run(). The agent invokes Acuity Scheduling tools and returns structured results.
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="Acuity Scheduling_assistant",
model_client=OpenAIChatCompletionClient(model="gpt-4o"),
tools=tools,
)
result = await agent.run("List recent Acuity Scheduling data")
print(result.messages[-1].content) 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.
McpWorkbenchis 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=workbenchto your agent. State is preserved across multiple tool calls within the same session.
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="Acuity Scheduling_assistant",
model_client=OpenAIChatCompletionClient(model="gpt-4o"),
workbench=workbench,
)
result = await agent.run("List recent Acuity Scheduling data")
print(result.messages[-1].content) Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Acuity Scheduling. 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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