Are query results limited by size?

Yes. The underlying Redshift Data API imposes soft constraints; for enormous responses, you might receive a paginated `NextToken`. While this MCP server auto-handles some response collection, queries returning over a few megabytes of raw JSON should be pre-filtered using `LIMIT` or aggregated to avoid token constraints in the LLM.

Can I use standard IAM credentials or do I need specific AWS roles?

The integration accepts standard static IAM keys (`AWS_ACCESS_KEY_ID` & `AWS_SECRET_ACCESS_KEY`), provided they hold sufficient IAM inline or attached policies allowing use of `redshift-data:*` operations targeting your exact Cluster ARN.

Why does `execute_sql` only return a statement ID instead of the data?

Because the Amazon Redshift Data API is strictly asynchronous. Queries often take seconds to minutes. Returning the `statement_id` instantly allows the AI to continue parsing conversations or interacting with other systems without locking up, executing `get_results` at a later time when the query officially succeeds.

Amazon Redshift MCP Server for LangChain 7 tools — connect in under 2 minutes

Name: Amazon Redshift
Availability: InStock
Author: Vinkius

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 7 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect Amazon Redshift through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

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

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "amazon-redshift": {
            "transport": "streamable_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,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using Amazon Redshift, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

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Ed25519Audit chain

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Amazon Redshift MCP Server

Connect your Amazon Redshift data warehouse securely to your AI agent utilizing the AWS Redshift Data API. This integration empowers your AI interface to natively run aggregations, explore massive schemas, and retrieve historical executing query logs asynchronously without requiring persistent DB connection pools, JDBC drivers, or complex networking configurations.

LangChain's ecosystem of 500+ components combines seamlessly with Amazon Redshift through native MCP adapters. Connect 7 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

What you can do

Execute Asynchronous SQL — Direct the AI to execute standard SQL commands (execute_sql), including complex SELECT aggregations, table creation (DDL), or data mutation (DML). Since it uses the Data API, long-running queries will process in the background.
Poll & Retrieve Results — Ask the agent to proactively monitor the execution lifecycle (statement_status) of dispatched query IDs and retrieve the dataset rows (get_results) securely into your terminal upon completion.
Schema & Table Discovery — Understand the database structure dynamically by generating lists of available schemas (list_schemas) or looking up column metadata metrics for specific tables (describe_table).
Statement Histories — Perform audits assessing previously submitted query structures and track analytical workloads running on your configured cluster (list_statements).

The Amazon Redshift MCP Server exposes 7 tools through the Vinkius. Connect it to LangChain in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Amazon Redshift to LangChain via MCP

Follow these steps to integrate the Amazon Redshift MCP Server with LangChain.

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 7 tools from Amazon Redshift via MCP

Why Use LangChain with the Amazon Redshift MCP Server

LangChain provides unique advantages when paired with Amazon Redshift through the Model Context Protocol.

The largest ecosystem of integrations, chains, and agents. combine Amazon Redshift MCP tools with 500+ LangChain components

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

Memory and conversation persistence let agents maintain context across Amazon Redshift queries for multi-turn workflows

Amazon Redshift + LangChain Use Cases

Practical scenarios where LangChain combined with the Amazon Redshift MCP Server delivers measurable value.

RAG with live data: combine Amazon Redshift tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query Amazon Redshift, synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain Amazon Redshift tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every Amazon Redshift tool call, measure latency, and optimize your agent's performance

Amazon Redshift MCP Tools for LangChain (7)

These 7 tools become available when you connect Amazon Redshift to LangChain via MCP:

describe_table

Retrieves column metadata for a table

execute_sql

This is an asynchronous operation that returns a statement ID. Executes a SQL statement using the Redshift Data API

get_results

Retrieves the results of a completed SQL statement

list_schemas

Lists all database schemas in Redshift

list_statements

Lists recent SQL statements executed in the cluster

list_tables

Lists all tables in a specific schema

statement_status

Checks the execution status of a SQL statement

Example Prompts for Amazon Redshift in LangChain

Ready-to-use prompts you can give your LangChain agent to start working with Amazon Redshift immediately.

"List all active tables present inside the 'reporting_schema' schema."

"Describe the column parameters for 'user_cohorts' in the reporting schema."

"Run a query to fetch the sum of sales amounts where region is 'APAC' from the 'quarterly_revenue' table."

Troubleshooting Amazon Redshift MCP Server with LangChain

Common issues when connecting Amazon Redshift to LangChain through the Vinkius, and how to resolve them.

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Amazon Redshift + LangChain FAQ

Common questions about integrating Amazon Redshift MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.