How to Use the OceanBase MCP in LangChain

Q: How do I connect the OceanBase MCP Server to a LangChain agent?

Install langchain-mcp-adapters and use the MultiServerMCPClient to point to the Vinkius endpoint. This registers all ten OceanBase tools with your agent, allowing it to invoke operations like listclusters during chain runs.

Q: Can a LangChain runnable chain execute multiple OceanBase tools in a single run?

Yes, you can build chains where the output of listinstances feeds directly into getinstancedetails. LangChain manages the state between these tool calls, enabling multi-step diagnostic workflows.

Build multi-step database orchestration chains that inspect and manage your OceanBase clusters using LangChain agents.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect OceanBase MCP to LangChain

Create your Vinkius account to connect OceanBase to LangChain and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

GDPR Free for Subscribers

Setup OceanBase with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Multi-step cluster diagnostics with LangChain chains

`list_clusters` lets your LangChain agent find active OceanBase clusters and feed the output directly into subsequent chain links. The agent inspects the cluster topology first, then automatically passes the target cluster ID to `get_cluster_details` to confirm node health. This chaining mechanism eliminates manual scripting when troubleshooting distributed database setups. You trace the entire execution path in LangSmith to see exactly which cluster IDs your LangChain agent selected during the run.

Automated tenant provisioning with LangChain agents

`list_tenants` queries your OceanBase environment to evaluate resource distribution across your active database tenants. When a tenant approaches its limit, the LangChain agent executes `get_tenant_details` to fetch current CPU and memory allocations. The agent uses these metrics to decide if a new tenant needs provisioning. By feeding this structural data directly into your LangChain decision loops, you automate tenant management without writing custom glue code.

Real-time database discovery via LangChain MCP Server

`list_databases` pulls the exact database schema inventory directly into your LangChain runnable sequence. Your agent uses this live inventory to map out data locations before initiating complex schema migrations. This direct integration means your LangChain pipelines always work with fresh structural metadata instead of stale configuration files. You get a direct, observable pipeline that maps your entire OceanBase storage layout on demand.

Setup guide

Set up OceanBase MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes OceanBase tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "oceanbase-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent OceanBase transactions"
    })
    print(result["messages"][-1].content)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by OceanBase. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect OceanBase now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about OceanBase MCP in LangChain

Install `langchain-mcp-adapters` and use the `MultiServerMCPClient` to point to the Vinkius endpoint. This registers all ten OceanBase tools with your agent, allowing it to invoke operations like `list_clusters` during chain runs.

Yes, you can build chains where the output of `list_instances` feeds directly into `get_instance_details`. LangChain manages the state between these tool calls, enabling multi-step diagnostic workflows.

Use LangSmith to trace the exact inputs and outputs of tools like `get_resource_stats`. This reveals the precise JSON payload your LangChain agent sent and the raw performance metrics returned by OceanBase.

Yes, the LangChain MCP client aggregates tools from multiple servers into a unified agent interface. You can combine OceanBase cluster management tools with external cloud APIs in a single reasoning loop.

The server only accesses structural metadata such as database names from `list_databases` and resource stats from `get_resource_stats`. No raw table records or user data are ever read or transmitted, and the Vinkius sandbox ensures all metadata remains isolated.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript