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🤖 Enable AI assistants (Claude, Cursor) to monitor, diagnose, and query Amazon Redshift using this MCP server and the A
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": "uvx",
"args": ["redshift-utils-mcp"],
"env": {
"REDSHIFT_CLUSTER_ID":"your-cluster-id",
"REDSHIFT_DATABASE":"your_database_name",
"REDSHIFT_SECRET_ARN":"arn:aws:secretsmanager:...",
"AWS_REGION": "us-east-1"
}
}
}
Use the Claude CLI to add the server configuration:
claude mcp add redshift-utils-mcp \
-e REDSHIFT_CLUSTER_ID="your-cluster-id" \
-e REDSHIFT_DATABASE="your_database_name" \
-e REDSHIFT_SECRET_ARN="arn:aws:secretsmanager:..." \
-e AWS_REGION="us-east-1" \
-- uvx redshift-utils-mcp
stdio transport type.uvx run redshift_utils_mcp). Ensure any necessary environment variables are available to the command being run.| Resource URI Pattern | Description | Example URI |
|---|---|---|
/scripts/{script_path} | Retrieves the raw content of a SQL script file from the server's sql_scripts directory. | /scripts/health/disk_usage.sql |
redshift://schemas | Lists all accessible user-defined schemas in the connected database. | redshift://schemas |
redshift://wlm/configuration | Retrieves the current Workload Management (WLM) configuration details. | redshift://wlm/configuration |
redshift://schema/{schema_name}/tables | Lists all accessible tables and views within the specified {schema_name}. | redshift://schema/public/tables |
Replace {script_path} and {schema_name} with the actual values when making requests.
Accessibility of schemas/tables depends on the permissions granted to the Redshift user configured via REDSHIFT_SECRET_ARN.
| Tool Name | Description | Key Parameters (Required*) | Example Invocation |
|---|---|---|---|
handle_check_cluster_health | Performs a health assessment of the Redshift cluster using a set of diagnostic SQL scripts. | level (optional), time_window_days (optional) | use_mcp_tool("redshift-admin", "handle_check_cluster_health", {"level": "full"}) |
handle_diagnose_locks | Identifies active lock contention and blocking sessions in the cluster. | min_wait_seconds (optional) | use_mcp_tool("redshift-admin", "handle_diagnose_locks", {"min_wait_seconds": 10}) |
handle_diagnose_query_performance | Analyzes a specific query's execution performance, including plan, metrics, and historical data. | query_id* | use_mcp_tool("redshift-admin", "handle_diagnose_query_performance", {"query_id": 12345}) |
handle_execute_ad_hoc_query | Executes an arbitrary SQL query provided by the user via Redshift Data API. Designed as an escape hatch. | sql_query* | use_mcp_tool("redshift-admin", "handle_execute_ad_hoc_query", {"sql_query": "SELECT ..."}) |
handle_get_table_definition | Retrieves the DDL (Data Definition Language) statement (SHOW TABLE) for a specific table. | schema_name, table_name | use_mcp_tool("redshift-admin", "handle_get_table_definition", {"schema_name": "public", ...}) |
handle_inspect_table | Retrieves detailed information about a specific Redshift table, covering design, storage, health, and usage. | schema_name, table_name | use_mcp_tool("redshift-admin", "handle_inspect_table", {"schema_name": "analytics", ...}) |
handle_monitor_workload | Analyzes cluster workload patterns over a specified time window using various diagnostic scripts. | time_window_days (optional), top_n_queries (optional) | use_mcp_tool("redshift-admin", "handle_monitor_workload", {"time_window_days": 7}) |
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