How to Use the Amazon Redshift MCP in LangChain

Q: How does my LangChain agent run a query against Amazon Redshift?

It's a three-step process. First, the agent calls executesql with your SQL. It then uses the returned ID to repeatedly call statementstatus until the query finishes. Finally, it calls getresults to fetch the data.

Q: Can a LangChain agent figure out table columns in Amazon Redshift on its own?

Yes. It can use listschemas to see all available schemas, listtables to find tables within a schema, and describetable to get the specific column names and types for any table. This is how it constructs accurate queries.

Q: What's the best way to handle long-running queries in Amazon Redshift with LangChain?

The tools are designed for this. Since executesql is asynchronous, your chain won't time out. You just need to create a simple polling loop that checks statementstatus before trying to fetch data with getresults.

Build LangChain agents that chain SQL queries and reason over your Amazon Redshift data warehouse.

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Connect Amazon Redshift MCP to LangChain

Create your Vinkius account to connect Amazon Redshift 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.

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Setup Amazon Redshift with LangChain

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Run and Check Asynchronous Queries

Your agent can now run complex analytics against petabyte-scale data. Use `execute_sql` to kick off a query without blocking. This returns a statement ID, not the data itself, which is key for long-running jobs. Then, build a loop in your chain that polls `statement_status` until the job is complete. Once it's done, a final call to `get_results` pulls the data. This pattern lets your agent handle warehouse-scale jobs without timing out.

Explore Your Data Warehouse

Before writing any SQL, your agent needs to know what it's working with. The `list_schemas` and `list_tables` tools let it map out the entire database structure on its own. For any given table, `describe_table` provides the column names and data types. Your agent uses this info to construct valid, targeted SQL, reducing errors and guesswork. It's how a reasoning chain learns the lay of the land.

Build Self-Governing LangChain Agents

Give your agents the ability to monitor their own work. The `list_statements` tool shows a history of recent queries, including their status and duration. You can design a chain where one agent runs queries while another acts as an auditor. The auditor agent uses `list_statements` to check for failed or long-running queries and can trigger corrective actions. This MCP server gives you the building blocks for that.

Setup guide

Set up Amazon Redshift 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 Amazon Redshift 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({
    "amazon-redshift-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 Amazon Redshift transactions"
    })
    print(result["messages"][-1].content)

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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 Amazon Redshift. 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 Amazon Redshift MCP in LangChain

It's a three-step process. First, the agent calls `execute_sql` with your SQL. It then uses the returned ID to repeatedly call `statement_status` until the query finishes. Finally, it calls `get_results` to fetch the data.

Yes. It can use `list_schemas` to see all available schemas, `list_tables` to find tables within a schema, and `describe_table` to get the specific column names and types for any table. This is how it constructs accurate queries.

The tools are designed for this. Since `execute_sql` is asynchronous, your chain won't time out. You just need to create a simple polling loop that checks `statement_status` before trying to fetch data with `get_results`.

This MCP Server is managed and sandboxed by Vinkius. You get one endpoint token for auth, and it works out of the box with the agent toolkit. No need to manage database drivers or credentials directly in your code.

The server handles your SQL query text, table and schema metadata, and the actual data rows returned by your queries. Vinkius isolates each request in an ephemeral sandbox. Your credentials are never exposed, and all communication is secured via your single Vinkius API token.

Use it with your favorite AI tools

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