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 OpenAI Agents SDK 7 tools — connect in under 2 minutes

Name: Amazon Redshift
Availability: InStock
Author: Vinkius

openai.github.io/openai-agents-python/mcp

Built by Vinkius GDPR 7 Tools SDK

The OpenAI Agents SDK enables production-grade agent workflows in Python. Connect Amazon Redshift through Vinkius and your agents gain typed, auto-discovered tools with built-in guardrails. no manual schema definitions required.

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 agents import Agent, Runner
from agents.mcp import MCPServerStreamableHttp

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MCPServerStreamableHttp(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as mcp_server:

        agent = Agent(
            name="Amazon Redshift Assistant",
            instructions=(
                "You help users interact with Amazon Redshift. "
                "You have access to 7 tools."
            ),
            mcp_servers=[mcp_server],
        )

        result = await Runner.run(
            agent, "List all available tools from Amazon Redshift"
        )
        print(result.final_output)

asyncio.run(main())

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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.

The OpenAI Agents SDK auto-discovers all 7 tools from Amazon Redshift through native MCP integration. Build agents with built-in guardrails, tracing, and handoff patterns. chain multiple agents where one queries Amazon Redshift, another analyzes results, and a third generates reports, all orchestrated through Vinkius.

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 OpenAI Agents SDK 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 OpenAI Agents SDK via MCP

Follow these steps to integrate the Amazon Redshift MCP Server with OpenAI Agents SDK.

Install the SDK

Run pip install openai-agents in your Python environment

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Run the script

Save the code above and run it: python agent.py

Explore tools

The agent will automatically discover 7 tools from Amazon Redshift

Why Use OpenAI Agents SDK with the Amazon Redshift MCP Server

OpenAI Agents SDK provides unique advantages when paired with Amazon Redshift through the Model Context Protocol.

Native MCP integration via `MCPServerSse`, pass the URL and the SDK auto-discovers all tools with full type safety

Built-in guardrails, tracing, and handoff patterns let you build production-grade agents without reinventing safety infrastructure

Lightweight and composable: chain multiple agents and MCP servers in a single pipeline with minimal boilerplate

First-party OpenAI support ensures optimal compatibility with GPT models for tool calling and structured output

Amazon Redshift + OpenAI Agents SDK Use Cases

Practical scenarios where OpenAI Agents SDK combined with the Amazon Redshift MCP Server delivers measurable value.

Automated workflows: build agents that query Amazon Redshift, process the data, and trigger follow-up actions autonomously

Multi-agent orchestration: create specialist agents. one queries Amazon Redshift, another analyzes results, a third generates reports

Data enrichment pipelines: stream data through Amazon Redshift tools and transform it with OpenAI models in a single async loop

Customer support bots: agents query Amazon Redshift to resolve tickets, look up records, and update statuses without human intervention

Amazon Redshift MCP Tools for OpenAI Agents SDK (7)

These 7 tools become available when you connect Amazon Redshift to OpenAI Agents SDK 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 OpenAI Agents SDK

Ready-to-use prompts you can give your OpenAI Agents SDK 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 OpenAI Agents SDK

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

MCPServerStreamableHttp not found

Ensure you have the latest version: pip install --upgrade openai-agents

Agent not calling tools

Make sure your prompt explicitly references the task the tools can help with.

Amazon Redshift + OpenAI Agents SDK FAQ

Common questions about integrating Amazon Redshift MCP Server with OpenAI Agents SDK.

How does the OpenAI Agents SDK connect to MCP?

Use MCPServerSse(url=...) to create a server connection. The SDK auto-discovers all tools and makes them available to your agent with full type information.