Amazon Redshift MCP Server
Equip your AI to directly query, analyze, and manage your petabyte-scale data warehouse via the serverless AWS Redshift Data API.
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What is the Amazon Redshift MCP Server?
The Amazon Redshift MCP Server gives AI agents like Claude, ChatGPT, and Cursor direct access to Amazon Redshift via 7 tools. Equip your AI to directly query, analyze, and manage your petabyte-scale data warehouse via the serverless AWS Redshift Data API. Powered by the Vinkius - no API keys, no infrastructure, connect in under 2 minutes.
Built-in capabilities (7)
Tools for your AI Agents to operate Amazon Redshift
Ask your AI agent "List all active tables present inside the 'reporting_schema' schema." and get the answer without opening a single dashboard. With 7 tools connected to real Amazon Redshift data, your agents reason over live information, cross-reference it with other MCP servers, and deliver insights you would spend hours assembling manually.
Works with Claude, ChatGPT, Cursor, and any MCP-compatible client. Powered by the Vinkius - your credentials never touch the AI model, every request is auditable. Connect in under two minutes.
Why teams choose Vinkius
One subscription gives you access to thousands of MCP servers - and you can deploy your own to the Vinkius Edge. Your AI agents only access the data you authorize, with DLP that blocks sensitive information from ever reaching the model, kill switch for instant shutdown, and up to 60% token savings. Enterprise-grade infrastructure and security, zero maintenance.
Build your own MCP Server with our secure development framework →Vinkius works with every AI agent you already use
…and any MCP-compatible client
Amazon Redshift MCP Server capabilities
7 toolsRetrieves column metadata for a table
This is an asynchronous operation that returns a statement ID. Executes a SQL statement using the Redshift Data API
Retrieves the results of a completed SQL statement
Lists all database schemas in Redshift
Lists recent SQL statements executed in the cluster
Lists all tables in a specific schema
Checks the execution status of a SQL statement
What the Amazon Redshift MCP Server 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 about the Amazon Redshift MCP Server
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.
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Connect Amazon Redshift with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
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Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Give your AI agents the power of Amazon Redshift MCP Server
Production-grade Amazon Redshift MCP Server. Verified, monitored, and maintained by Vinkius. Ready for your AI agents — connect and start using immediately.