Skip to content
MCP-Bridge
DatabasesAuto-generatedScore: 46

Amazon Redshift MCP Server

Amazon Redshift is a fully managed, petabyte-scale cloud data warehouse service provided by Amazon Web Services (AWS).

Quick Start Summary

The Amazon Redshift MCP server is a Model Context Protocol bridge that connects AI assistants — including Claude Desktop, Cursor, Windsurf, and VS Code Copilot — to the Amazon Redshift API through natural language. It exposes 10 API endpoints as callable tools, such as GET_AcceptReservedNodeExchange, POST_AcceptReservedNodeExchange, GET_AddPartner, and more. No authentication is required — setup takes approximately 30 seconds. The server uses STDIO transport and can be installed by running npx -y @mcp/amazonaws-com-redshift. This integration is sourced from the auto Amazon Redshift OpenAPI specification (v2012-12-01) and has a quality score of 46/99 (fair documentation coverage).

10Endpointstools mapped
NoneAuthopen access
46/99Qualityfair
~30 secSetupno auth
View Hosted Config URLClaude Desktop Setup Guide

Server Details

Category
Databases
Authentication
None
Endpoints
10 operations
Transport
STDIO
Spec Version
v2012-12-01
Install Command
npx -y @mcp/amazonaws-com-redshift

Environment Variables

AMAZON_REDSHIFT_API_KEY

Example: your_amazon_redshift_api_key

Top Endpoints

GET
/#Action=AcceptReservedNodeExchange

GET_AcceptReservedNodeExchange

POST
/#Action=AcceptReservedNodeExchange

POST_AcceptReservedNodeExchange

GET
/#Action=AddPartner

GET_AddPartner

POST
/#Action=AddPartner

POST_AddPartner

GET
/#Action=AssociateDataShareConsumer

GET_AssociateDataShareConsumer

Own this API?

Verify ownership of this listing to control the description, configuration details, and documentation links. Choose between free manual verification or instant premium placement.

Option 1: Free Verification

Slow manual review. Requires creating a GitHub issue with verified documentation or domain verification.

  • • Verified badge on page
  • • Standard search sorting
  • • 2-3 business days review
Start Free Claim →
Instant & Boosted

Option 2: Featured Upgrade($9/mo)

Instant verification plus premium styling, featured badges, and directory placement boost.

  • • ★ Featured star & amber highlight border
  • • Top of directory search placement
  • • Instant activation via claim token

📖 Detailed MCP Integration Guide

A technical breakdown of capabilities, agent workflows, and security/configuration best practices.

⚡Capabilities & Use Cases
Amazon Redshift is a fully managed, petabyte-scale cloud data warehouse service provided by Amazon Web Services (AWS). Its API serves as the foundational management plane for a service designed specifically for high-performance online analytical processing (OLAP) workloads, enabling organizations to run complex queries against massive datasets using standard SQL and existing business intelligence tools. Core capabilities exposed through this API include comprehensive cluster lifecycle management—from provisioning and configuration to scaling and termination—alongside advanced features like automated snapshots, data sharing across accounts, and security controls. Typical enterprise use cases encompass serving as the central repository for enterprise data warehousing (EDW), powering business intelligence (BI) and reporting platforms, enabling big data analytics, and supporting machine learning workflows by providing a performant, SQL-accessible store for training data. The API operations provided, such as AcceptReservedNodeExchange, AddPartner, AssociateDataShareConsumer, and AuthorizeClusterSecurityGroupIngress, are critical for administrators managing costs, collaborative data ecosystems, and granular network security policies.
🤖AI Agent Value
When this API is exposed as a set of tools via the Model Context Protocol (MCP) to an AI coding assistant, its value is transformed from a static management interface into a dynamic, conversational operations hub. The AI agent acts as an intelligent intermediary, translating high-level natural language instructions into precise, executable API calls. This integration dramatically lowers the operational barrier for developers and data engineers, allowing them to focus on outcomes rather than memorizing complex API schemas or manual console navigation. The AI can maintain contextual awareness of the Redshift environment, understand the implications of actions (e.g., the cost impact of a node exchange or the security scope of an authorization rule), and execute multi-step workflows that would otherwise require multiple manual steps and deep service knowledge. This turns routine and complex administration tasks into fluid, prompt-driven interactions, accelerating DevOps velocity and reducing human error in critical data infrastructure management.
💬Example Workflows
Practical workflow examples enabled by this MCP server include instructing the AI agent to "review and accept the pending reserved node exchange proposal for our analytics cluster to optimize costs," which the agent would execute by invoking the AcceptReservedNodeExchange action. A developer could issue a command like "set up a secure data share with our partner account '123456789012' for the marketing_campaigns schema," prompting the agent to orchestrate the sequence of AuthorizeDataShare and AddPartner calls. For security and governance, a natural language request such as "temporarily authorize the IP range 203.0.113.0/24 to connect to the 'dev' cluster security group" would translate into an automated call to AuthorizeClusterSecurityGroupIngress. Furthermore, for ongoing data product management, the agent could be tasked to "list all active data shares and their current consumer associations for the quarterly audit," dynamically querying the system state and presenting a synthesized report.
🛡️Security & Auth
It is critical to note that while the API endpoint reference lists basic HTTP methods, all actual calls to the Amazon Redshift API require robust authentication and authorization via AWS Identity and Access Management (IAM). Developers must configure the MCP server environment with IAM user or role credentials that possess the necessary permissions for the intended Redshift actions. Strict adherence to the principle of least privilege is paramount; the associated IAM policy should grant only the specific Redshift actions required for the AI agent's intended workflow, avoiding broad permissions like "redshift:*". Credentials must be managed securely, ideally leveraging environment variables or a secrets manager rather than hardcoding, and the MCP server should be deployed in a secure environment with appropriate network controls. Always assume that any action executed by the AI agent via this API has significant operational and security implications, and therefore, workflows in production environments should incorporate human approval steps for critical operations like security group modifications or cluster scaling.

Similar APIs

Other APIs in the Databases category.

PostgreSQL (MCP)

Query and manage PostgreSQL databases directly from your AI agent. Read schemas, run queries, and manage data.

Database Credentials

Notion API

The Notion API is a comprehensive RESTful interface provided by Notion, the popular all-in-one workspace platform, enabling programmatic interaction with its rich set of collaborative objects. It grants developers and automated systems the ability to read, create, update, and manage core Notion entities such as blocks (the fundamental building blocks of content like text, lists, and media), databases (structured tables with properties), pages (containers for content and databases), and comments. Typical use cases span enterprise and consumer scenarios, including automating team workflows, syncing data between Notion and other business systems (like CRM, project management, or analytics tools), building custom dashboards, generating dynamic reports, and enhancing content collaboration through programmatic updates. Organizations leverage this API to break down data silos, enforce process automation, and create tailored integrations that extend Notion's native capabilities for specific departmental or cross-functional needs.

Amazon CloudWatch Application Insights

Amazon CloudWatch Application Insights is a specialized observability service provided by Amazon Web Services (AWS) designed to simplify the monitoring and troubleshooting of applications, particularly those built on Microsoft IIS and .NET frameworks running on EC2 instances or within Elastic Beanstalk environments. Its core capability lies in automatically discovering application components, analyzing correlated metrics, logs, and traces to identify anomalies, and then surfacing actionable insights that pinpoint the root cause of common operational issues. By integrating seamlessly with other AWS services like CloudWatch, AWS X-Ray, and AWS Systems Manager, it provides a unified view of application health, reducing the mean time to resolution (MTTR) for performance degradations and errors. The typical use case spans enterprise environments managing distributed microservices or monolithic .NET applications, where teams need to proactively detect issues such as memory leaks, high CPU utilization, or specific application errors without manually configuring complex monitoring dashboards and alarms.

Application Auto Scaling

The Application Auto Scaling API, provided by Amazon Web Services (AWS), is a robust service designed to automate the scaling of computing resources for a wide array of AWS services, ensuring optimal performance, availability, and cost efficiency. Its core capability is to define policies that automatically adjust the provisioned capacity of supported resources in response to changing demand, as measured by CloudWatch metrics or predefined schedules. Beyond the initially listed resources, it supports scaling for Amazon DynamoDB tables and global secondary indexes, Amazon ECS services running on Fargate or EC2, Amazon ElastiCache replication groups, Amazon Neptune clusters, Amazon SageMaker endpoint variants, and custom resources via the AWS Lambda-backed scalable target. This makes it a central tool for architects and DevOps engineers in building resilient, self-optimizing cloud architectures. Typical enterprise use cases include dynamically adjusting the number of Aurora read replicas to handle database query load spikes, scaling ECS task counts during peak traffic for a microservices application, or optimizing costs by scaling down SageMaker inference endpoints during off-hours.

Related MCP Server Integrations

PostgreSQL (MCP) MCP Setup

Query and manage PostgreSQL databases directly from your AI agent. Read schemas, run queries, and manage data.

DatabasesConfigure →

Notion API MCP Setup

The Notion API is a comprehensive RESTful interface provided by Notion, the popular all-in-one workspace platform, enabling programmatic interaction with its rich set of collaborative objects. It grants developers and automated systems the ability to read, create, update, and manage core Notion entities such as blocks (the fundamental building blocks of content like text, lists, and media), databases (structured tables with properties), pages (containers for content and databases), and comments. Typical use cases span enterprise and consumer scenarios, including automating team workflows, syncing data between Notion and other business systems (like CRM, project management, or analytics tools), building custom dashboards, generating dynamic reports, and enhancing content collaboration through programmatic updates. Organizations leverage this API to break down data silos, enforce process automation, and create tailored integrations that extend Notion's native capabilities for specific departmental or cross-functional needs.

DatabasesConfigure →

Amazon CloudWatch Application Insights MCP Setup

Amazon CloudWatch Application Insights is a specialized observability service provided by Amazon Web Services (AWS) designed to simplify the monitoring and troubleshooting of applications, particularly those built on Microsoft IIS and .NET frameworks running on EC2 instances or within Elastic Beanstalk environments. Its core capability lies in automatically discovering application components, analyzing correlated metrics, logs, and traces to identify anomalies, and then surfacing actionable insights that pinpoint the root cause of common operational issues. By integrating seamlessly with other AWS services like CloudWatch, AWS X-Ray, and AWS Systems Manager, it provides a unified view of application health, reducing the mean time to resolution (MTTR) for performance degradations and errors. The typical use case spans enterprise environments managing distributed microservices or monolithic .NET applications, where teams need to proactively detect issues such as memory leaks, high CPU utilization, or specific application errors without manually configuring complex monitoring dashboards and alarms.

DatabasesConfigure →

Application Auto Scaling MCP Setup

The Application Auto Scaling API, provided by Amazon Web Services (AWS), is a robust service designed to automate the scaling of computing resources for a wide array of AWS services, ensuring optimal performance, availability, and cost efficiency. Its core capability is to define policies that automatically adjust the provisioned capacity of supported resources in response to changing demand, as measured by CloudWatch metrics or predefined schedules. Beyond the initially listed resources, it supports scaling for Amazon DynamoDB tables and global secondary indexes, Amazon ECS services running on Fargate or EC2, Amazon ElastiCache replication groups, Amazon Neptune clusters, Amazon SageMaker endpoint variants, and custom resources via the AWS Lambda-backed scalable target. This makes it a central tool for architects and DevOps engineers in building resilient, self-optimizing cloud architectures. Typical enterprise use cases include dynamically adjusting the number of Aurora read replicas to handle database query load spikes, scaling ECS task counts during peak traffic for a microservices application, or optimizing costs by scaling down SageMaker inference endpoints during off-hours.

DatabasesConfigure →

AWS Cost Explorer Service MCP Setup

The AWS Cost Explorer API, provided by Amazon Web Services, serves as the programmatic backbone for the Cost Explorer service, a powerful tool designed to help organizations visualize, understand, and manage their AWS cloud spending. At its core, this API enables developers and financial operations (FinOps) teams to move beyond the web console and directly query their cost and usage data, unlocking the ability to build custom dashboards, automated reports, and sophisticated cost management applications. Its capabilities range from retrieving high-level aggregated data, such as monthly service costs or daily usage totals, to drilling down into granular, resource-level details, including the specific write operations of a DynamoDB table or the data transfer metrics of an EC2 instance. This granular access is critical for enterprises implementing showback/chargeback models, identifying optimization opportunities, and enforcing budget guardrails across complex, multi-account AWS environments.

DatabasesConfigure →