How to Use the Azure Functions Invoke MCP in OpenAI Agents SDK

Q: How do I configure Azure Functions Invoke with OpenAI Agents SDK?

Install openai-agents, then configure the MCP server using the streamable HTTP transport class. Pass the server instance directly to your Agent constructor in the mcpservers list to auto-discover the tools.

Q: Does Azure Functions Invoke support async calls in OpenAI Agents SDK?

Yes. The invokefunction tool runs synchronously from the agent's perspective, but the SDK handles the HTTP transport asynchronously using Python's async context managers.

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Connect Azure Functions Invoke MCP to OpenAI Agents SDK

Create your Vinkius account to connect Azure Functions Invoke 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 Azure Functions Invoke with OpenAI Agents SDK

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Trigger serverless compute from OpenAI Agents SDK

Your production agents need a secure way to run heavy backend logic. This MCP server exposes the `invoke_function` tool, allowing your agent to run backend tasks on Azure without managing permanent servers. You supply the endpoint, and the agent triggers the execution when it needs to run calculations or update database records. It gets back raw JSON or text to use in the next step of the run.

Guardrails for the `invoke_function` tool

Running serverless code in production requires strict control. OpenAI Agents SDK lets you inspect the payload before the agent calls `invoke_function`, stopping malformed parameters before they reach Azure. If the agent decides to trigger the function, the entire event is logged in your OpenAI dashboard. You can trace exactly what data went in and what JSON came back.

Cache the Azure Functions Invoke MCP tool list

In production, latency kills the user experience. You can boot up this server using `MCPServerStreamableHttp` and set `cacheToolsList=True` to prevent the agent from querying the server on every message. The agent keeps the schema for `invoke_function` in memory, making tool calls instantaneous. This keeps your agent fast while keeping its execution profile completely secure.

Setup guide

Set up Azure Functions Invoke 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 Azure Functions Invoke tools at runtime.
3

Create your Agent

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

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Azure Functions Invoke 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="Azure Functions Invoke Agent",
            instructions="You have access to Azure Functions Invoke 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 Azure Functions Invoke MCP in OpenAI Agents SDK

Install `openai-agents`, then configure the MCP server using the streamable HTTP transport class. Pass the server instance directly to your Agent constructor in the `mcp_servers` list to auto-discover the tools.

Yes. The `invoke_function` tool runs synchronously from the agent's perspective, but the SDK handles the HTTP transport asynchronously using Python's async context managers.

Yes, you can. You can pass the tool to one specific agent in your handoff chain, ensuring only your database-specialized agent can trigger the serverless function.

The server waits for the Azure Function to finish executing before returning. If your function takes more than a few seconds, configure your HTTP client timeout parameters during the server setup.

Your Azure Function payloads and response JSON are processed in an ephemeral V8 sandbox. We never write your serverless payloads to persistent storage, ensuring zero-trust data handling.

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