Copus

Search human-curated content recommendations from real people who explain why resources are valuable - The Internet Treasure Map

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Copus MCP Server

An MCP (Model Context Protocol) server that allows AI assistants to search and retrieve human-curated content recommendations from Copus.

What is Copus?

Copus is a human-curated content discovery platform — "The Internet Treasure Map". Unlike SEO-driven search results, Copus surfaces recommendations from real people who explain why content is valuable.

Each curation includes:

  • Curator's personal note — Why they recommend this
  • Curator credentials — Why they're qualified to recommend this
  • Original source URL — The actual content being recommended
  • AI-enhanced metadata — Key takeaways, target audience, problem solved
  • Engagement metrics — Views, saves, comments from the community

What This MCP Server Enables

This server gives AI assistants access to Copus's curated content database. Instead of generic search results, your AI can find:

  • Tools and resources vetted by domain experts
  • Articles recommended by practitioners in the field
  • Hidden gems that real people found valuable enough to share

Compatible AI Platforms

This MCP server works with any AI platform that supports the Model Context Protocol:

  • Claude Desktop (Anthropic)
  • Claude Code (Anthropic)
  • Cursor (AI code editor)
  • Cline (VS Code extension)
  • Continue (VS Code/JetBrains extension)
  • Zed (Code editor)
  • Any other MCP-compatible AI platform

Installation

Quick Start (npx)

No installation required — run directly with npx:

npx copus-mcp-server

Global Installation

npm install -g copus-mcp-server

Then run:

copus-mcp-server

Local Installation

npm install copus-mcp-server

Configuration

Claude Desktop

Add to your Claude Desktop configuration file:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "copus": {
      "command": "npx",
      "args": ["copus-mcp-server"]
    }
  }
}

Or if installed globally:

{
  "mcpServers": {
    "copus": {
      "command": "copus-mcp-server"
    }
  }
}

Claude Code

Add to your Claude Code MCP settings:

{
  "mcpServers": {
    "copus": {
      "command": "npx",
      "args": ["copus-mcp-server"]
    }
  }
}

Cursor

Add to your Cursor MCP configuration (.cursor/mcp.json in your project or global settings):

{
  "mcpServers": {
    "copus": {
      "command": "npx",
      "args": ["copus-mcp-server"]
    }
  }
}

Cline (VS Code Extension)

Add to Cline's MCP settings in VS Code:

  1. Open VS Code Settings
  2. Search for "Cline MCP"
  3. Add the server configuration:
{
  "copus": {
    "command": "npx",
    "args": ["copus-mcp-server"]
  }
}

Continue (VS Code/JetBrains)

Add to your Continue configuration (~/.continue/config.json):

{
  "mcpServers": [
    {
      "name": "copus",
      "command": "npx",
      "args": ["copus-mcp-server"]
    }
  ]
}

Available Tools

search_curations

Search human-curated content recommendations on Copus.

Parameters:

  • query (string, required): Search keywords
  • limit (number, optional): Maximum results (default: 10, max: 50)

Returns: Array of curations with:

  • Title and description
  • Curator name and profile
  • Original source URL
  • Category and keywords
  • Engagement metrics (views, saves)

get_curation

Get detailed information about a specific curation.

Parameters:

  • id (string, required): Curation ID (UUID from search results)

Returns: Full curation details including:

  • Curator's personal recommendation note
  • Curator credentials
  • Key takeaways
  • Target audience
  • What problem this content solves
  • Full engagement metrics

Example Use Cases

Once configured, you can ask your AI assistant things like:

Learning Resources

"I want to learn Python, what resources should I check out?"

"Find me some recommended machine learning tutorials"

"What are the best resources for learning web development?"

Tools & Software

"What tools do designers recommend for wireframing?"

"Find me some AI tools that people actually use and recommend"

"What's a good free video editing software?"

Reading & Content

"Any good reads on creative writing?"

"Find me articles about productivity that people found valuable"

"What are some recommended newsletters about tech?"

Specific Topics

"Find watermark remover tools"

"What Linux tools do people recommend?"

"Show me personal growth content recommendations"

Example Response

When you search for "python tutorials", you might get:

{
  "query": "python tutorials",
  "totalResults": 5,
  "results": [
    {
      "id": "abc123...",
      "title": "Real Python - Python Tutorials",
      "description": "Comprehensive Python tutorials covering basics to advanced topics...",
      "originalSource": "https://realpython.com",
      "category": "Technology",
      "curator": "experienced_dev",
      "engagement": {
        "views": 150,
        "saves": 23
      }
    }
  ]
}

Why Use Copus Over Regular Search?

Regular SearchCopus Curations
SEO-optimized resultsHuman-selected recommendations
Algorithm-drivenExpert-vetted content
No context on qualityCurator explains why it's valuable
Anonymous sourcesKnown curator with credentials
Quantity-focusedQuality-focused

Development

Building from Source

git clone https://github.com/copus-io/copus-mcp-server.git
cd copus-mcp-server
npm install
npm run build

Running in Development

npm run dev

Testing

# Run the server
npm start

# In another terminal, test with MCP inspector or your AI platform

API Reference

This MCP server wraps the Copus public API:

  • Search API: https://copus.network/api/search?q={query}
  • Curation Details: https://copus.network/work/{id}?format=json
  • OpenAPI Spec: https://copus.network/.well-known/openapi.yaml
  • AI Plugin Manifest: https://copus.network/.well-known/ai-plugin.json

Links

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

  1. Fork the repository
  2. Create your feature branch (git checkout -b feature/amazing-feature)
  3. Commit your changes (git commit -m 'Add amazing feature')
  4. Push to the branch (git push origin feature/amazing-feature)
  5. Open a Pull Request

License

MIT License - see LICENSE for details.

Support

Built with love by the Copus team.

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