Redis Cloud API MCP Server

What does “Redis Cloud API” MCP Server do?

The Redis Cloud API MCP Server is a Model Context Protocol (MCP) implementation that enables AI assistants and MCP clients to interact with Redis Cloud resources through natural language. By serving as a bridge between large language models (LLMs) and the Redis Cloud API, it allows developers to manage accounts, subscriptions, and databases, as well as monitor tasks and configure resources, all within their development tools. This enhances productivity by automating complex cloud management tasks like provisioning databases, checking account status, or configuring cloud provider options, making Redis Cloud operations more accessible and efficient for developers using AI-driven workflows.

List of Prompts

No explicit prompt templates are mentioned in the repository or documentation.

List of Resources

• Account Information: Provides current Redis Cloud account details, including payment methods and configuration.
• Subscription Data: Exposes data for both Pro and Essential subscriptions, with support for pagination and detailed queries.
• Database Modules: Lists available Redis modules and features supported in the user’s account.
• Task Status: Returns information about ongoing and past tasks, such as deployment status and subscription changes.

List of Tools

• get_current_account: Retrieves details about the current Redis Cloud account.
• get_current_payment_methods: Lists all payment methods associated with the account.
• get_pro_subscriptions: Lists all Pro subscriptions.
• create_pro_subscription: Creates a new Pro subscription with configurable options.
• get_essential_subscriptions: Lists all Essential subscriptions (paginated).
• get_essential_subscription_by_id: Retrieves information about a specific Essential subscription.
• create_essential_subscription: Creates a new Essential subscription.
• delete_essential_subscription: Deletes an Essential subscription.
• get_database_modules: Lists Redis modules and database capabilities.
• get_pro_plans_regions: Fetches available regions across cloud providers.
• get_essentials_plans: Lists available Essential subscription plans.
• get_tasks: Lists all current tasks.
• get_task_by_id: Retrieves detailed information about a specific task.

Use Cases of this MCP Server

• Account Management: Quickly retrieve account and payment details, streamlining administrative operations for developers.
• Subscription Lifecycle: Automate the creation, listing, and deletion of Pro and Essential subscriptions, reducing manual steps in managing Redis Cloud environments.
• Database Provisioning: Enable developers to create and configure new Redis databases, including module selection, persistence, and networking, directly from their IDE or AI assistant.
• Cloud Provider Planning: Easily query available regions, pricing, and networking options across AWS, GCP, and Azure, aiding in deployment planning and resource optimization.
• Task Monitoring: Track the progress of deployments, subscription changes, or updates by listing and querying tasks, improving visibility and control over cloud operations.

How to set it up

Windsurf

No explicit setup instructions for Windsurf platform are provided.

Claude

1. Build the package:

   npm run build
   

2. Open Claude Desktop settings and navigate to the Developer tab (enable Developer Mode).
3. Click “Edit config” and open claude_desktop_config.json.
4. Add the MCP server configuration:

   {
     "mcpServers": {
       "mcp-redis-cloud": {
         "command": "node",
         "args": ["--experimental-fetch", "<absolute_path_to_project_root>/dist/index.js"],
         "env": {
           "API_KEY": "<redis_cloud_api_key>",
           "SECRET_KEY": "<redis_cloud_api_secret_key>"
         }
       }
     }
   }
   

5. Restart Claude Desktop to activate the server.

Securing API Keys: Use the env section in the configuration to supply API keys as environment variables.

Cursor

1. Build the package:

   npm run build
   

2. Open Cursor Settings, go to the MCP tab, and “Add new global MCP Server”.
3. Update the auto-opened mcp.json file:

   {
     "mcpServers": {
       "mcp-redis-cloud": {
         "command": "node",
         "args": ["--experimental-fetch", "<absolute_path_to_project_root>/dist/index.js"],
         "env": {
           "API_KEY": "<redis_cloud_api_key>",
           "SECRET_KEY": "<redis_cloud_api_secret_key>"
         }
       }
     }
   }
   

4. Save and restart Cursor.

Securing API Keys: Use the env property for sensitive data.

Cline

No explicit setup instructions for Cline platform are provided.

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "mcp-redis-cloud": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent can use this MCP as a tool with access to all its functions and capabilities. Remember to change “mcp-redis-cloud” to the actual name of your MCP server and update the URL accordingly.

Overview

Roots support: Not mentioned

Between these two tables, I would rate the Redis Cloud API MCP Server as a solid 7.5/10. It is well-documented, feature-rich, and open source with clear API key handling, but lacks explicit information on prompt templates, sampling, roots, and Windsurf/Cline setup.

MCP Score