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

Name: Amazon Redshift
Availability: InStock
Author: Vinkius

aws.amazon.com/redshift

Built by Vinkius GDPR ToolsFree

Equip your AI to directly query, analyze, and manage your petabyte-scale data warehouse via the serverless AWS Redshift Data API.

Vinkius AI Gateway supports streamable HTTP and SSE.

Works with every AI agent you already use

…and any MCP-compatible client

Amazon Redshift MCP Server: see your AI Agent in action

AI Agent→Vinkius→Amazon Redshift

You

Vinkius AI Gateway

GDPR·High Security·Kill Switch·Ultra-Low Latency·Plug and Play

Built-in capabilities (7)

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

What this connector unlocks

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.

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).

How it works

1. Authorize the Amazon Redshift MCP plugin from your connected extension hub.
2. Configure your serverless integration using standard AWS IAM principles. Supply an Access Key ID & Secret targeting your cluster, identifying the specific endpoint, Database Name, and DB User.
3. Chat seamlessly with your AI to prompt queries like "Describe the metadata for the 'public.events' table" or "Execute a query counting all sales processed yesterday."

Who is this for?

Data Analysts & Scientists — Execute ad-hoc exploratory aggregations through natural language prompts. Pull specific dataset metrics and schemas instantly into chat without switching to external SQL IDEs like DBeaver.
Backend Developers — Test schema migrations intuitively. Troubleshoot data integrations and check table state integrity interactively from the code editor during development.
Data Engineers — Audit Redshift cluster loads and verify execution lifecycles asynchronously for large reporting workloads directly connected to your conversational toolkit.

Frequently asked questions

Give your AI agents the power of Amazon Redshift

Access Amazon Redshift and 2,000+ MCP servers — ready for your agents to use, right now. No glue code. No custom integrations. Just plug Vinkius AI Gateway and let your agents work.

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