How to Use the Alpic MCP in OpenAI Agents SDK

Q: How do I connect Alpic to my OpenAI Agents SDK MCP pipeline?

Instantiate MCPServerStreamableHttp with your Alpic environment URL. Then, pass that instance into your OpenAI Agent constructor within the mcpservers list. Enabling cacheToolsList=True keeps your Alpic tool discovery fast.

Q: Can my OpenAI Agents SDK automate Alpic project creation?

Yes, your OpenAI agent calls createproject to spin up isolated environments for new clients. It then runs createenvironment to generate a dedicated Alpic connection URL. This lets your OpenAI Agents SDK scale its own infrastructure.

Q: How do I test Alpic configurations locally with OpenAI Agents SDK?

Yes, use gettunnelticket to route local Alpic traffic directly to your OpenAI agent. This lets you debug agent behavior inside your local IDE before running a full deployment.

Q: How does Alpic secure the API keys and environment variables used by OpenAI Agents SDK?

Alpic encrypts your API keys at rest and masks them during listvariables calls. Only the runtime container decodes the actual values when executing your tools. Your OpenAI Agents SDK never exposes these raw secrets in plain text.

Spin up and manage your entire Alpic MCP cluster directly from your OpenAI Agents SDK runtime.

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Connect Alpic MCP to OpenAI Agents SDK

Create your Vinkius account to connect Alpic 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 Alpic with OpenAI Agents SDK

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Run Alpic deployments from OpenAI Agents SDK

`deploy_environment` lets your OpenAI agent trigger new builds of your custom Alpic MCP Server tools. Your OpenAI agent calls this tool to push updates to staging, tracking the build via `get_deployment` to confirm it went live. You feed the resulting Alpic URL back into your `MCPServerStreamableHttp` configuration for OpenAI Agents SDK. This loop lets your OpenAI Agents SDK manage its own backend updates without human intervention.

Dynamic environment control with Alpic and OpenAI

`add_variable` injects fresh API keys or database connection strings directly into your running Alpic environments. Your OpenAI agent modifies these secrets on the fly to swap models or update third-party credentials. The agent inspects the active Alpic setup using `list_variables` before making changes. This prevents configuration drift across your production OpenAI Agents SDK MCP clusters.

Automated health checks inside OpenAI dashboards

`get_project_analytics` exposes real-time performance metrics of your running Alpic tools directly to your OpenAI agent. When latency spikes, the agent checks `get_deployment_logs` to isolate the bottleneck. These diagnostics match your OpenAI dashboard traces perfectly. You get instant visibility. You see which Alpic tool call caused the slowdown.

Setup guide

Set up Alpic 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 Alpic tools at runtime.
3

Create your Agent

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

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Alpic 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="Alpic Agent",
            instructions="You have access to Alpic tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Alpic. 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 Alpic MCP in OpenAI Agents SDK

Instantiate `MCPServerStreamableHttp` with your Alpic environment URL. Then, pass that instance into your OpenAI `Agent` constructor within the `mcp_servers` list. Enabling `cacheToolsList=True` keeps your Alpic tool discovery fast.

Yes, your OpenAI agent calls `create_project` to spin up isolated environments for new clients. It then runs `create_environment` to generate a dedicated Alpic connection URL. This lets your OpenAI Agents SDK scale its own infrastructure.

The OpenAI Agents SDK automatically queries your Alpic endpoint using `get_server_info` at startup. This exposes all active Alpic tools to your agent without manual schema mapping. Your OpenAI agent immediately understands what actions it can perform.

Yes, use `get_tunnel_ticket` to route local Alpic traffic directly to your OpenAI agent. This lets you debug agent behavior inside your local IDE before running a full deployment.

Alpic encrypts your API keys at rest and masks them during `list_variables` calls. Only the runtime container decodes the actual values when executing your tools. Your OpenAI Agents SDK never exposes these raw secrets in plain text.

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