context-awesome

Overview

Context Awesome is a Model Context Protocol server that exposes curated awesome lists as searchable resources. It provides access to over 8,500 awesome lists and more than 1 million curated resources, making it an invaluable tool for knowledge workers and researchers. The server offers three main tools: find_awesome_section for discovering relevant categories, search_awesome_items for full-text searching across resources, and get_awesome_items for fetching items from specific lists. It can be deployed both as a hosted MCP service or locally for offline use.

Try asking AI

After installing, here are 5 things you can ask your AI assistant:

• find_awesome_section

  Discover sections/categories across awesome lists matching a query

  Try:Find sections related to graph databasesTry:Search for machine learning awesome sections
• search_awesome_items

  Full-text search across individual items (tools/libraries/resources)

  Try:Search for Python web frameworksTry:Find React UI libraries
• get_awesome_items

  Fetch items from a known list + section, token-budgeted

  Try:Get items from the awesome-react listTry:Retrieve JavaScript frameworks from the awesome-javascript repo

Comparable tools

Installation

{
  "mcpServers": {
    "context-awesome": {
      "url": "https://www.context-awesome.com/api/mcp"
    }
  }
}

claude mcp add --transport http context-awesome https://www.context-awesome.com/api/mcp

{
  "mcpServers": {
    "context-awesome": {
      "command": "npx",
      "args": ["-y", "context-awesome-mcp", "serve", "--transport", "stdio"],
      "env": {
        "CONTEXT_AWESOME_API_HOST": "https://api.context-awesome.com"
      }
    }
  }
}

FAQ

What awesome lists are included?

The server provides access to over 8,500 awesome lists on GitHub with more than 1 million curated resources, covering topics from machine learning to design tools.

Can I use this offline?

Yes, you can run the MCP server locally using the stdio transport method, which doesn't require internet connectivity after initial setup.

Compare context-awesome with