How to Use the vCita MCP in AutoGen

Q: What payment information does AutoGen use with vCita?

The system uses both invoice lists (listclientinvoices) and transaction records (listrecordedpayments). Agents debate which financial record is most relevant for the current task.

Q: How do I check service availability using vCita with AutoGen?

You feed listofferedservices to the agent team. They then deliberate on how best to use that list to inform a booking decision, improving accuracy.

Get decisions on complex tasks using multi-agent debate with vCita and AutoGen.

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Connect vCita MCP to AutoGen

Create your Vinkius account to connect vCita 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 vCita with AutoGen

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Perplexity

Negotiate Appointments

Don't let agents just execute; make them deliberate. You can set up an agent team to discuss booking conflicts. One agent might check `list_scheduled_appointments`, while another debates the best time, finally calling `create_new_booking`. The system converges on a decision—a confirmed slot—after internal debate.

Review Operational Status

Build multi-perspective review systems. One agent checks general account health using `get_api_status`, while another reviews staff capacity via `list_staff_members`. They argue about which status is most critical. This process simulates human oversight, ensuring nothing gets missed.

Determine Pricing Consensus

Need to finalize a client setup? Set up agents to debate the pricing. One agent might check `list_price_estimates`, while another reviews current payments using `list_recorded_payments`. They negotiate the final, agreed-upon rate. The result is a consensus on the correct action.

Setup guide

Set up vCita 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 vCita 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="vCita_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent vCita 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="vCita_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent vCita 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 vCita. 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 vCita MCP in AutoGen

Agents debate the best path for scheduling. One might challenge an existing appointment using `cancel_appointment`, while another confirms a new slot using `create_new_booking`. The final decision is consensus-driven.

Yes. You can set up agents to debate whether a new client needs to be added, using `list_crm_clients` as the starting point for their deliberation process.

The system uses both invoice lists (`list_client_invoices`) and transaction records (`list_recorded_payments`). Agents debate which financial record is most relevant for the current task.

You feed `list_offered_services` to the agent team. They then deliberate on how best to use that list to inform a booking decision, improving accuracy.

The server touches CRM Customer records and Scheduled Appointments. All multi-agent outputs respect the secure boundaries of the MCP Server sandbox.

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