How to Use the vCita MCP in CrewAI

Q: How do I list services using vCita with CrewAI?

You assign the listofferedservices tool to a specialized agent. This agent runs, gathers all bookable service data, and passes it to the team's memory for later analysis.

Q: Can I check client details using vCita with CrewAI?

Yes. A dedicated 'Client Agent' uses getclientdetails when prompted. The output is then shared across the crew, allowing other agents to build upon that specific data point.

Q: Does the vCita MCP Server support viewing staff members with CrewAI?

It does. You give a team member the liststaffmembers tool. This lets them autonomously pull and organize your entire internal directory for resource allocation.

Q: How do I handle payments using vCita with CrewAI?

The 'Finance Agent' uses tools like listrecordedpayments. The crew can then check that the payment status matches what was recorded in the client's profile.

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Create your Vinkius account to connect vCita 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 vCita with CrewAI

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Perplexity

Team Management and Client Data Retrieval

If you need to find a client's history, the 'Data Agent' calls `get_client_details`. The 'Analysis Agent' then takes that output and generates a summary report. You get specialized roles working together. The 'Research Agent' can pull comprehensive lists of customers using `list_crm_clients`, which are then passed to the moderator agent for review.

Automating Scheduling and Service Listing

To schedule a service, one specialized agent uses `create_new_booking`. Another 'Validation Agent' simultaneously calls `list_offered_services` to confirm the availability of that service ID. If the booking needs adjustment, the 'Action Agent' can use `cancel_appointment` and then restart the process by calling another scheduling tool.

Complex Billing and Payment Workflows

The 'Finance Agent' pulls payment history using `list_recorded_payments`. It compares this against invoices generated by `list_client_invoices` to flag discrepancies. This collaboration allows you to check client estimates via `list_price_estimates`, ensuring that all billing actions are cross-referenced before any final report is created.

Setup guide

Set up vCita 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 vCita tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

    task = Task(
        description="List recent vCita 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 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 CrewAI

You assign the `list_offered_services` tool to a specialized agent. This agent runs, gathers all bookable service data, and passes it to the team's memory for later analysis.

Yes. A dedicated 'Client Agent' uses `get_client_details` when prompted. The output is then shared across the crew, allowing other agents to build upon that specific data point.

This MCP Server touches client information, booking records, and payment transaction details. The multi-agent system allows different parts of your application to read these types independently.

It does. You give a team member the `list_staff_members` tool. This lets them autonomously pull and organize your entire internal directory for resource allocation.

The 'Finance Agent' uses tools like `list_recorded_payments`. The crew can then check that the payment status matches what was recorded in the client's profile.

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