How to Use the Celigo integrator.io MCP in OpenAI Agents SDK

Q: How do I connect the Celigo integrator.io MCP Server to my OpenAI Agents SDK?

Install the SDK with pip install openai-agents and pass the Vinkius HTTP URL to MCPServerStreamableHttpParams. Register the server in your Agent constructor using the mcpservers list. The agent automatically discovers the tools like listintegrations during startup.

Run and monitor your Celigo integrator.io flows directly inside production-grade OpenAI Agents SDK pipelines.

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Connect Celigo integrator.io MCP to OpenAI Agents SDK

Create your Vinkius account to connect Celigo integrator.io to OpenAI Agents SDK 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 Celigo integrator.io with OpenAI Agents SDK

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Run Celigo integrator.io flows from OpenAI Agents SDK

When your production agent spots a system mismatch, it shouldn't just alert you. This MCP Server lets your OpenAI Agents SDK system trigger the exact recovery path by executing `run_integration_flow` directly. The OpenAI Agents SDK verifies the Celigo flow status first with `get_flow_details`. This ensures you don't fire off duplicate runs and ruin your API quotas.

Automated error resolution in OpenAI pipelines

Production pipelines built with the OpenAI Agents SDK need to handle failures without human eyes on screens. By calling `list_integration_errors`, your agent scans for failing Celigo steps in real time. Once identified, the OpenAI Agents SDK uses `list_integration_flows` to map the broken step to the right recovery sequence. It's a closed loop that keeps Celigo data moving without dev intervention.

Dynamic connection auditing for OpenAI Agents SDK

Agents need to know if their target systems are actually online before pushing data. This MCP Server lets your OpenAI Agents SDK check Celigo health using `list_integration_connections`. If a Celigo connection is down, the OpenAI Agents SDK halts and routes the task to a human-in-the-loop agent.

Setup guide

Set up Celigo integrator.io MCP in OpenAI Agents SDK

Prerequisites

Python 3.10+ installed
openai-agents package (pip install openai-agents)
Active Vinkius subscription with a valid endpoint token

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all Celigo integrator.io tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives Celigo integrator.io tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Celigo integrator.io tools and returns structured results. Copy the full example on the right to get started.

agent.py

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="Celigo integrator.io Agent",
            instructions="You have access to Celigo integrator.io tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Common questions about Celigo integrator.io MCP in OpenAI Agents SDK

Install the SDK with `pip install openai-agents` and pass the Vinkius HTTP URL to `MCPServerStreamableHttpParams`. Register the server in your `Agent` constructor using the `mcp_servers` list. The agent automatically discovers the tools like `list_integrations` during startup.

Yes, you control this at the Vinkius gateway level or by defining strict system instructions in your OpenAI Agents SDK. This prevents the agent from triggering destructive actions like `run_integration_flow` if it only needs to read statuses.

The SDK native tracing and guardrails help you monitor tool calls. If `list_integration_flows` hits Celigo rate limits, the OpenAI execution loop handles the backoff based on your runner's retry policy.

No custom Python tool definitions are required. The SDK auto-discovers all eight Celigo tools, including `list_integration_imports` and `list_integration_exports`, directly from the hosted endpoint.

The server runs in a zero-trust, ephemeral V8 Isolate Sandbox on Vinkius. Your Celigo API keys and integration metadata never touch OpenAI's training servers; they are only used to execute the requested actions like `list_integration_connections`.

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