How to Use the Beeline MCP in OpenAI Agents SDK

Manage external staff in your OpenAI Agents SDK projects by pulling live data from Beeline.

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Connect Beeline MCP to OpenAI Agents SDK

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

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Query requisition and assignment data

Your agent fetches job data directly using `get_requisition` and `list_requisitions`. This keeps your production systems updated without manual input. Running `search_requisitions` lets your agent pinpoint specific roles. The built-in guardrails in your OpenAI Agents SDK ensure that these queries stay within your defined safety constraints.

Automate timesheet tracking for OpenAI Agents SDK

Feed your agent `list_timesheets` to monitor hours logged by contractors. It validates every entry against your established business logic. Once the agent identifies a specific record, it uses `get_timesheet` to pull the details. This setup prevents data gaps in your tracking workflows.

Connect Beeline to your agent logic

Your MCP server exposes `get_user_info` and `list_suppliers` to ground agent decisions in real-world vendor data. This provides the context needed for high-stakes coordination. Use `list_expenses` to keep a tight loop on project costs. Your agent processes this information immediately, keeping your budget tracking accurate.

Setup guide

Set up Beeline 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 Beeline tools at runtime.
3

Create your Agent

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

Run the agent

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

You initialize the connection using the MCPServerStreamableHttp class. Just point your agent at the Vinkius URL and pass it into the agent constructor.

Yes, it supports auto-discovery natively. Your agent detects all ten tools as soon as you connect the server.

You can define which tools the agent accesses by managing your agent's tool list. This keeps your agent focused on specific tasks like requisition management.

We isolate your connection inside a V8 sandbox. Your credentials and contractor records never touch our persistent storage.

This server only touches assignment, timesheet, and requisition records. We use per-request tokens to ensure your agent only sees what you explicitly allow.

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