Rember MCP Server Integration

What does “Rember” MCP Server do?

The Rember MCP (Model Context Protocol) Server is designed to integrate Rember’s spaced repetition flashcard system with AI assistants, such as Claude. By acting as a bridge between Rember and AI clients, the server enables advanced workflows like creating flashcards directly from chats or documents, streamlining the process of studying and memorization. It exposes tools that allow LLMs to interact with Rember’s API, making it possible to generate and manage flashcards based on user interactions, notes, or uploaded content. This enhances development and learning workflows by automating flashcard creation and promoting efficient, AI-assisted study habits.

List of Prompts

No prompt templates are mentioned in the repository.

List of Resources

No explicit resources are listed in the repository.

List of Tools

• create_flashcards: This tool allows the AI to create flashcards in Rember by taking a list of notes (e.g., from a conversation or a PDF) and generating flashcards for each note via the Rember API. It enables users to quickly convert new information into a study-ready format by instructing the AI to “help me remember this” or “add to Rember.”

Use Cases of this MCP Server

• Creating Flashcards from Chats: After a conversation with an AI assistant like Claude, users can ask the MCP to generate flashcards from the discussed content, increasing retention of new knowledge.
• Converting PDFs to Flashcards: Users can prompt the AI to create flashcards from specific sections of uploaded PDFs, enabling efficient study of large documents.
• Automated Study Material Generation: Developers can automate the conversion of notes or learning materials into Rember flashcards, saving time and ensuring consistent study resources.
• Integration with AI Workflows: The MCP enables seamless integration of spaced repetition techniques into AI-driven learning and productivity tools.
• Personalized Learning: By leveraging user interactions and content, the server allows for personalized flashcard creation tailored to individual study needs.

How to set it up

Windsurf

1. Ensure you have Node.js installed.
2. Locate your Windsurf configuration file.
3. Add the Rember MCP server configuration in the mcpServers object.
4. Use the following JSON snippet, replacing YOUR_REMBER_API_KEY with your actual key:

   {
     "mcpServers": {
       "rember": {
         "command": "npx",
         "args": ["-y", "@getrember/mcp", "--api-key=YOUR_REMBER_API_KEY"]
       }
     }
   }
   

5. Save the configuration and restart Windsurf.
6. Verify the server is running and connected.

Claude

1. Obtain your Rember API key from the Rember settings page.
2. Open your claude_desktop_config.json.
3. Add the following under mcpServers:

   {
     "mcpServers": {
       "rember": {
         "command": "npx",
         "args": ["-y", "@getrember/mcp", "--api-key=YOUR_REMBER_API_KEY"]
       }
     }
   }
   

4. Save and restart Claude Desktop.
5. Confirm the connection in Claude’s interface.

Cursor

1. Make sure Node.js is installed.
2. Find Cursor’s MCP configuration file.
3. Insert the Rember MCP server details as shown:

   {
     "mcpServers": {
       "rember": {
         "command": "npx",
         "args": ["-y", "@getrember/mcp", "--api-key=YOUR_REMBER_API_KEY"]
       }
     }
   }
   

4. Save your changes and restart Cursor.
5. Verify the MCP server is active.

Cline

1. Install Node.js if needed.
2. Open your Cline configuration file.
3. Add the Rember MCP server configuration:

   {
     "mcpServers": {
       "rember": {
         "command": "npx",
         "args": ["-y", "@getrember/mcp", "--api-key=YOUR_REMBER_API_KEY"]
       }
     }
   }
   

4. Save and restart the Cline application.
5. Check that the MCP server is running.

Securing API Keys

It is recommended to secure your API keys using environment variables. Example configuration:

{
  "mcpServers": {
    "rember": {
      "command": "npx",
      "args": ["-y", "@getrember/mcp"],
      "env": {
        "REMBER_API_KEY": "YOUR_REMBER_API_KEY"
      },
      "inputs": {
        "api-key": "${REMBER_API_KEY}"
      }
    }
  }
}

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:

{
  "rember": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent can now use this MCP as a tool with access to all its functions and capabilities. Remember to change “rember” to the actual name of your MCP server and replace the URL with your own MCP server URL.

Overview

Based on the provided documentation and available information, the Rember MCP server is focused and well-documented for its primary use case (flashcard generation) but has only a single tool and lacks details on resources, prompts, or sampling support. It earns points for clear setup instructions and best practices, but its scope is narrow.

Our opinion

MCP Score: 6/10 — The server is valuable for users of Rember, especially for integrating with AI assistants, but it could be improved by offering more tools, resources, and documentation on advanced MCP features like prompts and sampling.

MCP Score