by vinodismyname
Redshift Utils offers essential Amazon Redshift database admin tools for health monitoring, query analysis, and automate
Provides Amazon Redshift database administration and monitoring through 40+ curated SQL scripts accessible via AWS Data API. Enables AI assistants to perform cluster health checks, query performance analysis, and diagnostic operations on Redshift warehouses.
Redshift Utils is a community-built MCP server published by vinodismyname that provides AI assistants with tools and capabilities via the Model Context Protocol. Redshift Utils offers essential Amazon Redshift database admin tools for health monitoring, query analysis, and automate It is categorized under databases, analytics data.
You can install Redshift Utils in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
MIT
Redshift Utils is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
Enable Claude to query your database directly using natural language
Example
Ask 'Show me top 10 customers by revenue this month' and get SQL results instantly
Eliminate manual SQL writing for ad-hoc queries, get insights 10x faster
Generate complex reports and analytics without leaving conversation
Example
Analyze sales trends, cohort retention, user behavior patterns conversationally
Democratize data access—non-technical team members can query databases
Understand database structure, relationships, and data models
Example
'Explain the user_orders table schema and its relationships'
Onboard engineers faster, explore unfamiliar databases efficiently
Share your MCP server with the developer community
Strong directory entry: Redshift Utils surfaces stars and publisher context so we could sanity-check maintenance before adopting.
Redshift Utils is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
We wired Redshift Utils into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
Redshift Utils reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
We evaluated Redshift Utils against two servers with overlapping tools; this profile had the clearer scope statement.
Redshift Utils is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
We wired Redshift Utils into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
Redshift Utils has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
Redshift Utils is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
We evaluated Redshift Utils against two servers with overlapping tools; this profile had the clearer scope statement.
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This project implements a Model Context Protocol (MCP) server designed specifically to interact with Amazon Redshift databases.
It bridges the gap between Large Language Models (LLMs) or AI assistants (like those in Claude, Cursor, or custom applications) and your Redshift data warehouse, enabling secure, standardized data access and interaction. This allows users to query data, understand database structure, and monitoring/diagnostic operations using natural language or AI-driven prompts.
This server is for developers, data analysts, or teams looking to integrate LLM capabilities directly with their Amazon Redshift data environment in a structured and secure manner.
handle_inspect_table) to gather detailed table metadata, statistics (like size, row counts, skew, stats staleness), and maintenance status.handle_execute_ad_hoc_query) to execute arbitrary SELECT queries against the Redshift database, enabling data retrieval based on LLM requests.handle_diagnose_query_performance) to retrieve and analyze the execution plan, metrics, and historical data for a specific query ID.handle_inspect_table) to perform a comprehensive inspection of a table, including design, storage, health, and usage.handle_check_cluster_health) to perform a basic or full health assessment of the cluster using various diagnostic queries.handle_diagnose_locks) to identify and report on current lock contention and blocking sessions.handle_monitor_workload) to analyze cluster workload patterns over a time window, covering WLM, top queries, and resource usage.handle_get_table_definition) to retrieve the SHOW TABLE output (DDL) for a specified table.Software:
uv (recommended package manager) or pipInfrastructure & Access:
redshift-data:*) and access the specified Secrets Manager secret (secretsmanager:GetSecretValue).CONNECT to the database, SELECT on target tables, SELECT on relevant system views like pg_class, pg_namespace, svv_all_schemas, svv_tables, `svv_table_info``). Using a role with the principle of least privilege is strongly recommended. See Security Considerations.Credentials:
Your Redshift connection details are managed via AWS Secrets Manager, and the server connects using the Redshift Data API. You need:
These details will be configured via environment variables as detailed in the Configuration section.
The easiest way to install the Redshift Utils MCP Server is directly from PyPI:
# Using pip
pip install redshift-utils-mcp
# Using uv (recommended)
uv pip install redshift-utils-mcp
Alternatively, you can install from the source repository:
# Clone the repository
git clone https://github.com/vinodismyname/redshift-utils-mcp.git
cd redshift-utils-mcp
# Install using uv (recommended)
uv sync
# Or install using pip
pip install -e .
Set Environment Variables:
This server requires the following environment variables to connect to your Redshift cluster via the AWS Data API. You can set these directly in your shell, using a systemd service file, a Docker environment file, or by creating a .env file in the project's root directory (if using a tool like uv or python-dotenv that supports loading from .env).
Example using shell export:
export REDSHIFT_CLUSTER_ID="your-cluster-id"
export REDSHIFT_DATABASE="your_database_name"
export REDSHIFT_SECRET_ARN="arn:aws:secretsmanager:us-east-1:123456789012:secret:your-redshift-secret-XXXXXX"
export AWS_REGION="us-east-1" # Or AWS_DEFAULT_REGION
# export AWS_PROFILE="your-aws-profile-name" # Optional
Example .env file (see .env.example):
# .env file for Redshift MCP Server configuration
# Ensure this file is NOT committed to version control if it contains secrets. Add it to .gitignore.
REDSHIFT_CLUSTER_ID="your-cluster-id"
REDSHIFT_DATABASE="your_database_name"
REDSHIFT_SECRET_ARN="arn:aws:secretsmanager:us-east-1:123456789012:secret:your-redshift-secret-XXXXXX"
AWS_REGION="us-east-1" # Or AWS_DEFAULT_REGION
# AWS_PROFILE="your-aws-profile-name" # Optional
Required Variables Table:
| Variable Name | Required | Description | Example Value |
|---|---|---|---|
REDSHIFT_CLUSTER_ID | Yes | Your Redshift cluster identifier. | my-redshift-cluster |
REDSHIFT_DATABASE | Yes | The name of the database to connect to. | mydatabase |
REDSHIFT_SECRET_ARN | Yes | AWS Secrets Manager ARN for Redshift credentials. | arn:aws:secretsmanager:us-east-1:123456789012:secret:mysecret-abcdef |
AWS_REGION | Yes | AWS region for Data API and Secrets Manager. | us-east-1 |
AWS_DEFAULT_REGION | No | Alternative to AWS_REGION for specifying the AWS region. | us-west-2 |
AWS_PROFILE | No | AWS profile name to use from your credentials file (~/.aws/...). | my-redshift-profile |
Note: Ensure the AWS credentials used by Boto3 (via environment, profile, or IAM role) have permissions to access the specified REDSHIFT_SECRET_ARN and use the Redshift Data API (redshift-data:*).
After installation, you can run the server directly from the command line:
# If installed from PyPI
redshift-utils-mcp
# Or using uvx (no installation required)
uvx redshift-utils-mcp
Add the following configuration block to your mcp.json file:
{
"mcpServers": {
"redshift-utils-mcp": {
"command": "u
---
Run data quality queries to catch anomalies and inconsistencies
Example
Find duplicate records, missing values, orphaned foreign keys automatically
Maintain data integrity with less manual SQL work
Prerequisites
Time Estimate
15-30 minutes including configuration and testing
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
MCP server acts as bridge between Claude and database, translating natural language to SQL queries and returning results in structured format.
Protocols
Compatibility
✓ Use when
Use for ad-hoc data queries, exploratory analysis, report generation, schema exploration, and democratizing data access. Best for read-heavy analytics workloads.
✗ Avoid when
Avoid for production write operations, mission-critical transactions, real-time OLTP workloads, or when database contains sensitive PII without proper access controls. Use read replicas, not primary.