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 AutoGen 7 tools — connect in under 2 minutes

Name: Amazon Redshift
Availability: InStock
Author: Vinkius

microsoft.github.io/autogen/docs/tools/mcp-tools

Built by Vinkius GDPR 7 Tools Framework

Microsoft AutoGen enables multi-agent conversations where agents negotiate, delegate, and execute tasks collaboratively. Add Amazon Redshift as an MCP tool provider through Vinkius and every agent in the group can access live data and take action.

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 autogen_agentchat.agents import AssistantAgent
from autogen_ext.tools.mcp import McpWorkbench

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with McpWorkbench(
        server_params={"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"},
        transport="streamable_http",
    ) as workbench:
        tools = await workbench.list_tools()
        agent = AssistantAgent(
            name="amazon_redshift_agent",
            tools=tools,
            system_message=(
                "You help users with Amazon Redshift. "
                "7 tools available."
            ),
        )
        print(f"Agent ready with {len(tools)} tools")

asyncio.run(main())

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* 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.

AutoGen enables multi-agent conversations where agents negotiate, delegate, and collaboratively use Amazon Redshift tools. Connect 7 tools through Vinkius and assign role-based access. a data analyst queries while a reviewer validates, with optional human-in-the-loop approval for sensitive operations.

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 AutoGen 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 AutoGen via MCP

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

Install AutoGen

Run pip install "autogen-ext[mcp]"

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Integrate into workflow

Use the agent in your AutoGen multi-agent orchestration

Explore tools

The workbench discovers 7 tools from Amazon Redshift automatically

Why Use AutoGen with the Amazon Redshift MCP Server

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

Multi-agent conversations: multiple AutoGen agents discuss, delegate, and collaboratively use Amazon Redshift tools to solve complex tasks

Role-based architecture lets you assign Amazon Redshift tool access to specific agents. a data analyst queries while a reviewer validates

Human-in-the-loop support: agents can pause for human approval before executing sensitive Amazon Redshift tool calls

Code execution sandbox: AutoGen agents can write and run code that processes Amazon Redshift tool responses in an isolated environment

Amazon Redshift + AutoGen Use Cases

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

Collaborative analysis: one agent queries Amazon Redshift while another validates results and a third generates the final report

Automated review pipelines: a researcher agent fetches data from Amazon Redshift, a critic agent evaluates quality, and a writer produces the output

Interactive planning: agents negotiate task allocation using Amazon Redshift data to make informed decisions about resource distribution

Code generation with live data: an AutoGen coder agent writes scripts that process Amazon Redshift responses in a sandboxed execution environment

Amazon Redshift MCP Tools for AutoGen (7)

These 7 tools become available when you connect Amazon Redshift to AutoGen 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 AutoGen

Ready-to-use prompts you can give your AutoGen 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 AutoGen

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

McpWorkbench not found

Install: pip install "autogen-ext[mcp]"

Amazon Redshift + AutoGen FAQ

Common questions about integrating Amazon Redshift MCP Server with AutoGen.

How does AutoGen connect to MCP servers?

Create an MCP tool adapter and assign it to one or more agents in the group chat. AutoGen agents can then call Amazon Redshift tools during their conversation turns.

Can different agents have different MCP tool access?

Yes. AutoGen's role-based architecture lets you assign specific MCP tools to specific agents, so a querying agent has different capabilities than a reviewing agent.