How to Use the Beamer MCP in OpenAI Agents SDK

Q: How do I connect the Beamer MCP Server to the OpenAI Agents SDK?

Install the SDK via pip and use MCPServerStreamableHttp pointing to your Vinkius endpoint. Pass this server instance into your Agent constructor inside an async context manager. The SDK automatically discovers all ten Beamer tools.

Publish Beamer updates and track user feedback safely with production-ready OpenAI Agents SDK guardrails.

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Connect Beamer MCP to OpenAI Agents SDK

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

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Safe Beamer post publishing with OpenAI Agents SDK

The `create_post` tool lets your OpenAI Agents SDK system publish product announcements to Beamer without manual intervention. You write the safety guardrails in Python to ensure no draft gets published before passing your internal staging checks. It's a foolproof way to prevent half-baked announcements. If an agent tries to modify an existing announcement using `update_post`, the SDK interceptors validate the content first. This setup keeps your public changelog clean while letting autonomous agents draft release notes directly from your codebase.

Multi-agent feedback routing via MCP Server

The `list_feedback` tool retrieves incoming customer comments from Beamer so your primary triage agent can analyze them. Once read, the SDK hands off the raw text to specialized sentiment analysis agents. You don't have to worry about routing logic because the framework handles the agent handoffs natively. If a customer leaves a critical bug report, your agent uses `get_feedback_details` to extract the full report. The entire lifecycle is tracked in your OpenAI dashboard, giving you complete visibility into how your agents handle user sentiment.

Automated analytics reporting and user audits

The `get_analytics` tool pulls engagement metrics directly from your Beamer dashboard to measure how your announcements perform. Your Python agent processes these numbers to determine if your latest feature release is hitting its adoption targets. To keep your user list clean, the agent uses `list_users` to cross-reference Beamer accounts with your internal database. By using this MCP Server, your Python agent runs these audits on a schedule, giving you automated weekly reports without manually exporting CSV files.

Setup guide

Set up Beamer 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 Beamer tools at runtime.
3

Create your Agent

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

Run the agent

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

Install the SDK via pip and use `MCPServerStreamableHttp` pointing to your Vinkius endpoint. Pass this server instance into your Agent constructor inside an async context manager. The SDK automatically discovers all ten Beamer tools.

Yes, you can enforce this using the SDK's built-in guardrails or by setting up specialized agents. For instance, you can build a writer agent that only has access to `create_post` and a separate support agent limited to `list_feedback`.

The SDK relies on your agent's retry logic and error handling when tools like `list_posts` hit API limits. You can catch these exceptions in your Python code to pause execution or back off before trying again.

Use the OpenAI tracing dashboard to inspect the exact payload sent to `update_post`. You will see the raw JSON inputs and outputs, making it easy to spot formatting errors or missing fields.

Yes, because Vinkius runs the MCP Server in a zero-trust, ephemeral V8 isolate. Your customer feedback gathered via `get_feedback_details` and user lists from `list_users` are never saved or stored on our servers.

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