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How to Use the Durianpay MCP in CrewAI

Deploy autonomous payment monitoring crews with CrewAI and this Durianpay MCP Server.

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Works with every AI agent you already use

…and any MCP-compatible client

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CrewAI

Connect Durianpay MCP to CrewAI

Create your Vinkius account to connect Durianpay 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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Autonomous revenue auditing

Assign an agent to run `quick_revenue_health_audit` every hour. It monitors your cash flow and alerts the rest of the crew if numbers dip. This setup replaces manual daily checks. The agent gathers the data, and the crew decides how to react.

Specialized payment investigation

Use `get_payment_details` to provide your research agent with deep context on disputed transactions. It pulls the full lifecycle status for your review. The research agent summarizes the findings for you. This allows your team to skip the manual search phase.

Customer list synchronization

Have your data agent run `list_registered_customers` to keep your internal CRM updated. It keeps the team aligned on active accounts. This runs as a background task within your crew. You get a fresh list of customers without lifting a finger.

Setup guide

Set up Durianpay MCP in CrewAI

Prerequisites

  • Python 3.10+ installed
  • crewai package (pip install crewai)
  • Active Vinkius subscription with a valid endpoint token
  1. 1

    Install CrewAI

    Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.

  2. 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. 3

    Kick off your crew

    Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke Durianpay tools as needed.

crew.py
from crewai import Agent, Task, Crew

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

task = Task(
    description="List recent Durianpay transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Durianpay MCP in CrewAI

Simply pass the server URL into the agent's mcps list. The tools become available to your agents immediately.
Yes. One agent can watch the transaction feed while another takes action on failed payments.
Agents share memory, so once one agent retrieves payment details, the rest of the crew can access that information.
The server uses ephemeral sessions. Your payment data is restricted to the specific task scope of the agent.
The server returns the appropriate error code. You can configure your agents to wait and retry the request.

Start using the Durianpay MCP today

We host it, we monitor it, we maintain it. You just paste one token.

Built & Managed by Vinkius 30s setup 10 tools

We've already built the connector for Durianpay. Just plug in your AI agents and start using Vinkius.

No hosting. No infrastructure. No complex setup.
All 10 tools are live and waiting. You're up and running in seconds.

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