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diff --git a/apps/docs/supermemory-mcp/introduction.mdx b/apps/docs/supermemory-mcp/introduction.mdx
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----
-title: 'About supermemory MCP'
-description: 'Carry your memories with you, using supermemory MCP'
----
-
-supermemory MCP is a lightweight **consumer-facing** component that allows you to carry your memories with you across any AI platform. It serves as a universal memory layer that enables Large Language Models (LLMs) to maintain persistent context and memories across different applications and sessions, solving the fundamental limitation of AI assistants forgetting everything between conversations.
-
-## What supermemory MCP is and how it works
-
-**supermemory MCP** functions as an open-source, universal memory system that bridges the gap between isolated AI applications through the Model Context Protocol (MCP). It operates as a **meta MCP server** that creates individual server instances for each user, providing seamless memory persistence across any MCP-compatible LLM client including ChatGPT, Claude, Windsurf, Cursor, and VS Code.
-
-The system works by running as an MCP server that communicates with MCP-compatible clients, storing and retrieving contextual information through the robust supermemory API infrastructure. When users interact with any connected AI application, the system automatically captures relevant information and makes it available to all other connected platforms through **semantic search and intelligent retrieval**.
-
-
-It is also a great example of how you can use supermemory to build your own consumer-facing applications.
-<Card title="supermemory MCP code" icon="github" href="https://github.com/supermemoryai/supermemory-mcp">
-    Code behind the scenes
-</Card>
-
-
-### Key consumer benefits
-
-- **No login requirements**: Access granted via unique, private URLs that serve as both identifier and API key
-- **Completely free**: Fully functional hosted service available at no cost
-- **One-command setup**: Single CLI installation: `npx install-mcp [URL] --client [CLIENT]`
-- **Universal compatibility**: Works seamlessly across multiple LLM clients and platforms
-
-
-### Core workflow process
-
-1. User interacts with any MCP-compatible AI client 
-2. The client connects to supermemory MCP server via a unique, private URL
-3. During conversations, relevant information is stored using the `addToSupermemory` action
-4. When context is needed, the `searchSupermemory` action retrieves relevant memories
-5. The AI assistant accesses this persistent context regardless of which platform is being used
-
-## Building applications with supermemory
-
-supermemory MCP demonstrates building consumer applications with supermemory through its **API-first architecture**. The system acts as a "customer" of the underlying supermemory API infrastructure, showcasing how developers can leverage the platform to create their own memory-enabled applications.
-
-### Supported platforms
-
-- **Claude Desktop**: Direct SSE connection support
-- **Cursor IDE**: Global MCP server configuration via `~/.cursor/mcp.json`
-- **Windsurf**: Seamless integration for AI-powered development
-- **VS Code**: Compatible with various AI coding extensions
-- **Cline/Roo-Cline**: Full MCP protocol support
-- **Any MCP-compatible application**: Universal compatibility through standard protocol implementation
-
-## Security and privacy considerations
-
-### Security model
-
-- **URL-based authentication**: Random URLs serve as access keys, eliminating traditional login complexity
-- **Session isolation**: Complete user data separation through unique URL paths
-- **Self-hosting option**: Full control over data and infrastructure for privacy-conscious users
-- **No authentication overhead**: Simplified access without traditional username/password systems
-
-### Privacy features
-
-- **Data isolation**: User memories completely separated by unique URLs
-- **Local control option**: Self-hosting capability for enterprise or sensitive use cases
-- **Secure infrastructure**: Built on Cloudflare's enterprise-grade security platform
-
-## Future developments and ecosystem
-
-The project represents a **breakthrough in AI memory portability**, with ongoing developments including:
-
-- **Infinite Chat API**: Enhanced inline memory management with conversation history
-- **Expanded client support**: More MCP clients adding SSE connection capabilities
-- **Enterprise features**: Advanced security and compliance options for business use
-- **Integration expansion**: Potential connections to Google Drive, Notion, OneDrive, and other productivity platforms
-
-supermemory MCP successfully demonstrates how to build compelling consumer-facing applications using supermemory's infrastructure, achieving massive adoption through exceptional user experience design combined with robust technical architecture. Its success validates the market demand for universal AI memory solutions and provides a compelling template for developers building their own memory-enabled applications.
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diff --git a/apps/docs/supermemory-mcp/setup.mdx b/apps/docs/supermemory-mcp/setup.mdx
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----
-title: 'Setup and usage'
-description: 'How to set up and use supermemory MCP'
----
-
-### Hosted setup (recommended)
-
-1. **Visit** https://mcp.supermemory.ai
-2. **Receive** automatically generated unique URL (e.g., `https://mcp.supermemory.ai/TN-IKxAcDdHWTJkMhtGLF/sse`)
-3. **Select** your MCP client from the dropdown menu
-4. **Copy** the generated installation command
-5. **Run** the command: `npx install-mcp [YOUR_URL] --client [CLIENT_NAME]`
-
-Or follow the client configuration example below.
-
-### Self-hosted configuration
-
-For users requiring complete data control:
-
-1. **Obtain API key** from https://console.supermemory.ai
-2. **Create `.env` file** with `SUPERMEMORY_API_KEY=your_key`
-3. **Clone repository**: `git clone https://github.com/supermemoryai/supermemory-mcp.git`
-4. **Configure MCP client** to connect to local server instance
-
-### Client configuration example
-
-1. For Clients that support `url` configuration:
-```json
-{
-  "mcpServers": {
-    "supermemory": {
-      "transport": "sse",
-      "url": "https://mcp.supermemory.ai/[USER_ID]/sse"
-    }
-  }
-}
-```
-
-2. Using `supergateway` with `npx`:
-```json
-{
-    "command": "npx",
-    "args": ["-y", "supergateway", "--sse", "YOUR_URL"]
-}
-```
-
-3. Using `supergateway` with `docker`:
-
-The equivalent MCP command would be:
-```json
-{
-  "mcpServers": {
-    "supermachineExampleDocker": {
-      "command": "docker",
-      "args": [
-        "run",
-        "-i",
-        "--rm",
-        "supercorp/supergateway",
-        "--sse",
-        "YOUR_SUPERMEMORY_URL"
-      ]
-    }
-  }
-}
-```
-
diff --git a/apps/docs/supermemory-mcp/technology.mdx b/apps/docs/supermemory-mcp/technology.mdx
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----
-title: 'Technical implementation details'
-description: 'Technical implementation details of supermemory MCP'
----
-
-The technical architecture prioritizes **simplicity and user experience** while maintaining robust functionality. Built as what the creators describe as "the simplest thing you'll see" - essentially a React Router application making fetch calls to the supermemory API - the entire system was developed and shipped in approximately 5 hours of actual work time.
-
-### Architecture components
-
-- **Backend API**: Built on top of the supermemory API (https://api.supermemory.ai/v3)
-- **Transport Layer**: Uses Server-Sent Events (SSE) for real-time communication
-- **Dynamic Server Generation**: Creates unique MCP server instances for each user via URL path parameters
-- **Session Management**: Maintains complete user isolation through unique URLs
-- **Infrastructure**: Hosted on Cloudflare using Durable Objects for persistent, long-running connections
-
-The system leverages **Cloudflare's infrastructure** with CPU-based billing, making it highly efficient since memory connections spend most time waiting between interactions rather than actively processing, resulting in minimal CPU usage despite potentially running for millions of milliseconds.
-
-## The two main components explained
-
-### addToSupermemory action
-
-This component **stores user information, preferences, and behavioral patterns** with sophisticated triggering mechanisms:
-
-**Trigger methods:**
-- **Explicit commands**: Direct user instructions like "remember this"
-- **Implicit detection**: Automatic identification of significant user traits, preferences, or patterns during conversations
-
-**Data types captured:**
-- Technical preferences and details (e.g., "My primary programming language is Python")
-- Project information and context (e.g., "I'm currently working on a project named 'Apollo'")
-- User behaviors and emotional responses
-- Personal facts, preferences, and decision-making patterns
-- Rich context including technical details and examples
-
-### searchSupermemory action
-
-This component **retrieves relevant information** from stored memories using advanced search capabilities:
-
-**Activation triggers:**
-- Explicit user requests for historical information
-- Contextual situations where past user choices would be helpful for current decisions
-- Automatic context enhancement based on conversation flow
-
-**Search capabilities:**
-- **Semantic matching**: Finds relevant details across related experiences using vector search
-- **Pattern recognition**: Identifies behavioral patterns and preferences
-- **Cross-session retrieval**: Accesses memories from previous conversations and platforms
-- **Intelligent filtering**: Returns most relevant context based on current conversation needs