Annanerdstation/Content-Saver-MCP-Server

Content Saver MCP Server + Next.js Web UI

A minimal, local storage MCP (Model Context Protocol) server with a Next.js Web UI that allows AI assistants (ChatGPT, Claude, Cursor) and humans to capture notes and links with automatic deduplication and smart tagging.

Features

MCP Server

• ✅ Save Notes - Store text notes with optional titles and tags
• ✅ Save Links - Store URLs with optional metadata
• ✅ URL Deduplication - Automatically prevents duplicate link entries
• ✅ Smart Tagging - Accepts AI-generated tags (tags provided by AI client)
• ✅ Search - Search items by query, tags, and date range
• ✅ List Recent - Get recently saved items
• ✅ Delete Items - Remove saved items by ID
• ✅ Local Storage - All data stored locally in JSON format

Web UI

• ✅ View Items - Browse all saved notes and links
• ✅ Search & Filter - Search by keyword, filter by type (notes/links), filter by tags
• ✅ Add Items - Create new notes or save links through the UI
• ✅ Item Details - View full item details in a side panel
• ✅ Delete Items - Remove items with confirmation
• ✅ Recent Items - Quick access to recently saved content
• ✅ AI Chat Assistant - Chat with an AI assistant that analyzes your saved content

Project Structure

Content-Saver-MCP-Server/
├── src/                    # MCP Server source code
│   ├── index.ts           # MCP server implementation
│   ├── storage.ts         # Storage layer
│   └── types.ts           # TypeScript types
├── web-ui/                 # Next.js Web UI
│   ├── app/               # Next.js app directory
│   │   ├── api/           # API routes (server-side only)
│   │   ├── page.tsx       # Main page
│   │   └── layout.tsx     # Root layout
│   ├── components/        # React components
│   └── lib/               # Utilities
└── dist/                  # Compiled MCP server

Installation

Prerequisites

• Node.js 18+
• npm or yarn

Setup

1. Clone the repository:

git clone <repository-url>
cd Content-Saver-MCP-Server

2. Install MCP Server dependencies:

npm install

3. Build MCP Server:

npm run build

4. Install Web UI dependencies:

cd web-ui
npm install

Usage

MCP Server

The MCP server runs on stdio and communicates via the MCP protocol:

npm start

Configure with AI clients:

• Claude Desktop: Add to ~/Library/Application Support/Claude/claude_desktop_config.json
• Cursor: Add to .cursor/mcp.json

{
  "mcpServers": {
    "content-saver": {
      "command": "node",
      "args": ["/path/to/Content-Saver-MCP-Server/dist/index.js"]
    }
  }
}

Web UI

Development:

cd web-ui
npm run dev

Visit http://localhost:3000 to use the web interface.

Production Build:

cd web-ui
npm run build
npm start

Deployment to Vercel

1. Push to GitHub (or your Git provider)

2. Import to Vercel:

   • Go to vercel.com
   • Import your repository
   • Vercel will auto-detect Next.js

3. Configure Build Settings:

   • Root Directory: web-ui
   • Build Command: npm run build
   • Output Directory: .next

4. Environment Variables (if needed):

   • MCP_SERVER_PATH: Path to MCP server (for server-side API routes)

5. Deploy

Data Storage

All items are stored locally in .content-saver/items.json in the project root directory.

Item Schema:

{
  id: string;
  type: "note" | "link";
  title?: string;
  body?: string;
  url?: string;
  tags: string[];
  createdAt: string;
  updatedAt?: string;
}

API Endpoints (Web UI)

All API routes are server-side only and communicate with the MCP storage layer:

• GET /api/items - Get all items
• POST /api/items - Save a new note or link
• GET /api/items/search - Search items with filters
• GET /api/items/recent - Get recent items
• DELETE /api/items/delete?id={id} - Delete an item
• POST /api/chat - Chat with AI assistant (analyzes saved content)

AI Chat Feature

The Web UI includes an AI chat assistant that can analyze your saved content:

Features:

• Ask questions about your saved items
• Get summaries and insights
• Identify topics and patterns
• Find relevant content

Setup (Optional):

For advanced AI analysis, configure OpenAI API:

1. Get API key from https://platform.openai.com/api-keys
2. Create .env.local in web-ui/ directory:

   OPENAI_API_KEY=your-api-key-here
   

3. Restart the dev server

Note: The chat works without an API key using basic pattern matching, but OpenAI integration provides much better analysis.

Development

MCP Server

# Build
npm run build

# Watch mode
npm run dev

# Run
npm start

Web UI

cd web-ui

# Development server
npm run dev

# Build
npm run build

# Production server
npm start

Requirements

• Node.js 18+
• TypeScript 5.3+
• Next.js 14+
• MCP-compatible AI client (for MCP server usage)

License

MIT