Amazon Redshift MCP Connector for Claude

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.