Does this MCP handle all AWS Redshift types?

The MCP is designed for Redshift clusters and allows you to specify hardware profiles like dc2, ds2, or ra3 when calculating cluster size.

What is the difference between optimization and resource management?

Optimization focuses on query execution, distribution styles, and concurrency scaling. Resource management handles the cluster's internal governance, like memory limits and backup schedules.

Do I need to know my exact data volume beforehand?

While the capabilities require data volume for accurate sizing, the MCP helps you plan even if your data is growing, allowing you to calculate based on current size and projected growth.

Can I use this MCP with my existing AI client?

Yes. You connect your preferred MCP-compatible client (like Claude, Cursor, or VS Code) once to the Vinkius catalog to gain access to this and other capabilities.

Is this capability just for sizing, or can it help with performance too?

It does both. You use calculate_cluster_size for initial sizing, and then use optimize_workload_performance to fine-tune the cluster for maximum query efficiency.

How do I determine the number of nodes needed?

You can use the calculate_cluster_size capability. Provide the total compressed data volume in TB and select your preferred node type (dc2, ds2, or ra3) to get the exact node count.

Can I configure WLM queues through this capability?

Yes, the configure_resource_management capability allows you to define up to 8 WLM queues and allocate memory percentages to ensure efficient workload management.

What is the difference between compound and interleaved sort keys?

When using optimize_workload_performance, you can choose between compound sort keys (prioritized sequence) or interleaved sort keys (multi-dimensional weight) depending on your query patterns.

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Connect Aws Redshift Cluster Calculator to your AI

Set it up once and it works in ChatGPT, Claude, Cursor, and any other client you love. It all runs safely in the background, so you can relax and focus on what really matters. Get deterministic sizing plans for your data warehouse architecture.

Ask AI about this Connector

Developed, maintained, and hosted by Vinkius.

MCP VERIFIED · PRODUCTION READY · VINKIUS GUARANTEED

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Works with modern AI clients that support MCP, including ChatGPT, Claude, Cursor, and more.

Your AI does the work. We keep it safe and sound.

No other AI tool gives you this. Your workflow runs in the client you already love, your account stays protected, and you decide what the agent can and cannot do. It is the whole reason to make the switch.

Each Connector lives inside our infrastructure, the same place where your workflow runs. The three things below are already part of it, working quietly in the background so you never have to think about them. Nothing to add, nothing to set up, nothing to worry about.

V8 Isolate01

Each Connector runs inside its own sealed environment.

Each Connector runs inside its own sealed V8 runtime — no access to your files, your system, or other servers. A Connector never sees past its own request.

Kill Switch02

One switch stops every agent. Instantly.

One action deactivates every Connector, revokes every token, and ends every session at once. AI Governance keeps the record.

FinOps per User03

Spending limits that pause instead of surprise.

Every user keeps their own connections and credentials, isolated from everyone else. Anything that would cross your spending limit is held for your approval, not billed.

Three controls, built into the layer your AI connects through.

Curated set · 3 capabilities

We ship only the actions that matter.

Capability set01 / 01

01-03

3 capabilities in this set.

Part of 3 available through AWS Redshift.

01
Calculate cluster size
Determines the fundamental hardware requirements for the Redshift cluster
02
Configure resource management
Sets up the internal governance of the cluster, including memory allocation and data backups
03
Optimize workload performance
Provides configuration for query execution, scaling, and data organization

Real work

What teams actually run

New Data Warehouse Buildout

You are starting from scratch. Use the MCP to calculate the initial node count based on your projected data volume and hardware needs.

Performance Bottleneck Analysis

Your current queries are slow. Use the optimization capability to check concurrency scaling slots and recommend better distribution styles.

Compliance and Backup Planning

You need to ensure data integrity. Use the resource management capability to set up mandatory snapshot schedules and memory allocations.

Migrating Legacy Data

You are moving from an older system. Use the MCP to calculate the size difference and determine the optimal new cluster configuration.

One connector, every AI

AWS Redshift works with the most popular AI clients.

These are the most popular clients, each with a step-by-step guide: one link, set up once, with governance and visibility built in. And because everything runs on the MCP standard, the same connection also works in any other compatible client — nothing to rebuild.

Building your own app? The connector is yours to use.

You don't need a client to put AWS Redshift to work: the same hosted connection plugs into your own applications and agent code, with the same governance on every request. Build with it, chat with it — one connection for both.

Observed, not estimated

879ms average. Fast in production.

AWS Redshift is checked daily against the live service.

Daily averagePeak 975ms

Oct 3Today

Fastest day: 630ms
Slowest day: 975ms
14-day trend: Stable+4%

Connect your client

One URL. Every client.

Activate the Connector, copy your link, and paste it into the client you already use. 3 capabilities arrive ready to run.

Preview access · not provider authentication

The vk_preview_* token belongs to Vinkius preview infrastructure. It lets Claude discover and display the capabilities of AWS Redshift, so you can see the experience inside your AI.

It does not authenticate your account with AWS Redshift. Actions requiring credentials or live account data may not run until you activate the Connector and authorize the service.

AWS Redshift Connector

You're all set. Choose your MCP client and follow the setup instructions.

Connector linkhttps://edge.vinkius.com/vk_preview_OJIVXy1WREFPoEekc6MBLkCmNskfCzYlKcrSevSj/mcp

Claude Desktop

Follow the steps below to connect in seconds.

1In Claude Desktop, open Settings → Connectors.
2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
3Click Add and start a new chat — AWS Redshift capabilities are ready to use.

Configuration · claude_desktop_config.jsonCopy

{
  "mcpServers": {
    "aws-redshift-cluster-calculator-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_OJIVXy1WREFPoEekc6MBLkCmNskfCzYlKcrSevSj/mcp"
    }
  }
}

Claude
ChatGPT
Cursor
VS Code
Windsurf
Claude Code
JetBrains
Cline

Step-by-step instructions for each client are in the guide. How to connect

Guided setup for Claude? Set up AWS Redshift in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work AWS Redshift owners hand off.

This MCP is essential for cloud architects, data engineers, and DevOps specialists. If your job involves designing, scaling, or migrating data warehouses, this capability provides the necessary technical depth. It lets you move beyond conceptual planning and generate concrete, executable infrastructure requirements.

01
Data Architect
Use this MCP to validate cluster sizing and determine optimal resource management policies before presenting a final design.
02
Data Engineer
Run performance optimization checks to ensure your data loading and querying processes will run efficiently on the target Redshift cluster.
03
Cloud DevOps Specialist
Automate the initial planning stages, generating the necessary parameters for infrastructure-as-code deployment.

FAQ

Questions AWS Redshift owners ask.

01
Does this MCP handle all AWS Redshift types?
The MCP is designed for Redshift clusters and allows you to specify hardware profiles like dc2, ds2, or ra3 when calculating cluster size.
02
What is the difference between optimization and resource management?
Optimization focuses on query execution, distribution styles, and concurrency scaling. Resource management handles the cluster's internal governance, like memory limits and backup schedules.
03
Do I need to know my exact data volume beforehand?
While the capabilities require data volume for accurate sizing, the MCP helps you plan even if your data is growing, allowing you to calculate based on current size and projected growth.
04
Can I use this MCP with my existing AI client?
Yes. You connect your preferred MCP-compatible client (like Claude, Cursor, or VS Code) once to the Vinkius catalog to gain access to this and other capabilities.
05
Is this capability just for sizing, or can it help with performance too?
It does both. You use calculate_cluster_size for initial sizing, and then use optimize_workload_performance to fine-tune the cluster for maximum query efficiency.
06
How do I determine the number of nodes needed?
You can use the calculate_cluster_size capability. Provide the total compressed data volume in TB and select your preferred node type (dc2, ds2, or ra3) to get the exact node count.
07
Can I configure WLM queues through this capability?
Yes, the configure_resource_management capability allows you to define up to 8 WLM queues and allocate memory percentages to ensure efficient workload management.
08
What is the difference between compound and interleaved sort keys?
When using optimize_workload_performance, you can choose between compound sort keys (prioritized sequence) or interleaved sort keys (multi-dimensional weight) depending on your query patterns.

Explore

More in Cloud Infrastructure

Connect AWS Redshift to Claude, Cursor, ChatGPT & more

Your AI does the work. We keep it safe and sound.

Each Connector runs inside its own sealed environment.

One switch stops every agent. Instantly.

Spending limits that pause instead of surprise.

We ship only the actions that matter.

01-03

Calculate cluster size

Configure resource management

Optimize workload performance

What teams actually run

New Data Warehouse Buildout

Performance Bottleneck Analysis

Compliance and Backup Planning

Migrating Legacy Data

Building your own app? The connector is yours to use.

AWS Redshift Connector

Claude Desktop

Data Architect

Data Engineer

Cloud DevOps Specialist

Does this MCP handle all AWS Redshift types?

What is the difference between optimization and resource management?

Do I need to know my exact data volume beforehand?

Can I use this MCP with my existing AI client?

Is this capability just for sizing, or can it help with performance too?

How do I determine the number of nodes needed?

Can I configure WLM queues through this capability?

What is the difference between compound and interleaved sort keys?

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