How to Use the OceanBase MCP in OpenAI Agents SDK

Q: How does this OceanBase MCP Server integrate with OpenAI Agents SDK?

You instantiate the server via MCPServerStreamableHttp and pass it to your agent constructor. The SDK automatically discovers all 10 tools, letting your agent call them as native functions.

Q: Does the OceanBase MCP Server support caching in OpenAI Agents SDK?

It does. Setting cacheToolsList=True in your configuration improves response times by memoizing the tool definitions during agent initialization.

Manage distributed OceanBase clusters directly within your OpenAI Agents SDK runtime for production-grade database operations.

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Connect OceanBase MCP to OpenAI Agents SDK

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

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Real-time cluster and instance visibility

Get deep insight into your distributed architecture without leaving your agent code. Use `list_clusters` and `list_instances` to map your entire environment instantly. These tools feed structural data directly into your agent state. You'll see exactly where your nodes sit, allowing your agent to make informed decisions about query routing and maintenance.

Tenant-level resource monitoring

Keep an eye on consumption metrics across your multi-tenant setup. Run `get_resource_stats` to pull live utilization numbers for specific tenants. Your agent can verify load before triggering operations. By calling `get_tenant_details`, you ensure the agent only interacts with authorized, active segments of your OceanBase deployment.

Database and workspace management

Navigate your data hierarchy programmatically using the OceanBase MCP Server. Use `list_databases` to inspect schemas and `get_workspaces` to organize your project structures. This gives your agent a clear view of where data lives. It turns your agent into an operator that can audit your entire storage footprint with precision.

Setup guide

Set up OceanBase 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 OceanBase tools at runtime.
3

Create your Agent

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

Run the agent

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

You instantiate the server via `MCPServerStreamableHttp` and pass it to your agent constructor. The SDK automatically discovers all 10 tools, letting your agent call them as native functions.

Yes. The SDK includes built-in guardrails that validate every tool call before execution. You control exactly what the agent can touch.

It does. Setting `cacheToolsList=True` in your configuration improves response times by memoizing the tool definitions during agent initialization.

All tool invocations are logged directly to your standard output or the OpenAI dashboard. You can trace every interaction from the initial request to the final database response.

Your credentials and database connection details never leave your local environment. The server only transmits the specific metadata requested by your agent.

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