How to Use the StarRocks MCP in LangChain

Q: What if I need to check StarRocks job status using LangChain?

You can use listjobs within a single chain step. Your agent checks the list, determines which job needs attention, and reports the findings without needing manual API calls.

LangChain: Build complex reasoning chains that execute StarRocks commands step by step.

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…and any MCP-compatible client

MCP Servers — Included with Plan

Connect StarRocks MCP to LangChain

Create your Vinkius account to connect StarRocks to LangChain — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup StarRocks with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity Vinkius runs on Gemini

Gemini

Grok

Building Multi-Step Data Pipelines with LangChain

You build workflows where the output of one tool feeds directly into the next. For example, you can first call `list_databases` to scope out available data sources. After identifying a database, your agent then runs `list_tables` and uses that list to formulate an accurate query via `execute_query`. It's all one chain.

Managing StarRocks Cluster State

Need to know if the cluster is healthy? Your ReAct agent can check node health by calling `list_nodes` or determine resource constraints using `get_storage_usage`. This keeps the entire data pipeline informed. If performance dips, you don't guess. You use `get_cluster_info` to confirm if the frontend nodes are behaving as expected before altering any code.

Observing StarRocks Schema and Views

Before querying petabytes of data, check what you're working with. Run `list_views` or `get_table_schema` to confirm column names and data types. This ability keeps your chains grounded. You don't just execute arbitrary SQL; you know exactly which tables are available in the specified database.

Setup guide

Set up StarRocks 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 StarRocks 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({
    "starrocks-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 StarRocks transactions"
    })
    print(result["messages"][-1].content)

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Common questions about StarRocks MCP in LangChain

LangChain lets your agent decide the query flow. It first uses tools like `list_databases` to understand the scope, then constructs and runs the precise SQL via `execute_query`. This makes multi-step data extraction predictable.

Yes. Since every tool call is a link in the chain, you get full observability into latency and tool inputs/outputs. This detailed tracing helps keep your complex data pipelines efficient.

You can use `list_jobs` within a single chain step. Your agent checks the list, determines which job needs attention, and reports the findings without needing manual API calls.

The framework supports aggregating tools from multiple servers. You can connect it to other data sources alongside StarRocks for unified, multi-source reasoning chains.

This server manages metadata about databases, tables, views, and job configurations. It doesn't directly process raw user content, but it handles structural definitions.

Use it with your favorite AI tools

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

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