About Context Awesome
Context Awesome is an MCP server and CLI for searching curated awesome lists and retrieving their entries. It helps research and knowledge-work agents find topic-specific tools, libraries, and learning resources from a shared backend.
Key features
- 01Discover matching list sections
- 02Full-text search across list items
- 03Fetch items by list and section
- 04MCP server and CLI share operations
Use cases
- 01Find libraries for a technical topic
- 02Gather learning resources for research
- 03Search curated references for an agent
MCP support
The MCP server exposes tools to discover awesome-list sections, search individual entries, and fetch entries from a selected list and section. It can be used through a hosted remote endpoint or local transport.
Configuration
Remote servers need no local install. Pick your client, then copy the config or install in one click.
https://www.context-awesome.com/api/mcpclaude mcp add --transport http bh-rat-context-awesome https://www.context-awesome.com/api/mcp{"mcpServers":{"bh-rat-context-awesome":{"type":"http","url":"https://www.context-awesome.com/api/mcp"}}}Open in Cursor{"servers":{"bh-rat-context-awesome":{"type":"http","url":"https://www.context-awesome.com/api/mcp"}}}Open in VS Code{"mcpServers":{"bh-rat-context-awesome":{"type":"http","url":"https://www.context-awesome.com/api/mcp"}}}{"mcpServers":{"bh-rat-context-awesome":{"serverUrl":"https://www.context-awesome.com/api/mcp"}}}Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.
Tools
2| Parameter | Type | Required | Description |
|---|---|---|---|
| limit | number | No | Maximum sections to return |
| query | string | Yes | Search terms for finding sections across awesome lists |
| confidence | number | No | Minimum confidence score (0-1) |
Details
From the project READMEcontext-awesome : awesome references for your agents 
A Model Context Protocol (MCP) server that provides access to all the curated awesome lists and their items. It can provide the best resources for your agent from sections of the 8500+ awesome lists on github and more then 1mn+ (growing) awesome row items.
What are Awesome Lists? Awesome lists are community-curated collections of the best tools, libraries, and resources on any topic - from machine learning frameworks to design tools. By adding this MCP server, your AI agents get instant access to these high-quality, vetted resources instead of relying on random web searches.
Perfect for :
- Knowledge worker agents to get the most relevant references for their work
- The source for the best learning resources
- Deep research can quickly gather a lot of high quality resources for any topic.
- Search agents
https://github.com/user-attachments/assets/babab991-e4ff-4433-bdb7-eb7032e9cd11
Two Ways to Use Context Awesome
| Mode | Install | Good for |
|---|---|---|
| MCP Server | point your agent at the hosted URL or spawn context-awesome-mcp | Claude Desktop, Cursor, Windsurf, VS Code — agents that natively speak MCP |
| CLI | npm install -g context-awesome | Scripts, shell workflows, editors without MCP support, CI jobs |
Both modes ship from the same npm package (context-awesome) and hit the same hosted backend.
MCP Tools
Every MCP tool has a 1:1 CLI subcommand — the server and the CLI expose the same operations.
| Tool | CLI equivalent | What it does |
|---|---|---|
find_awesome_section | context-awesome sections <query...> | Discover sections/categories across awesome lists matching a query |
search_awesome_items | context-awesome search <query...> | Full-text search across individual items (tools/libraries/resources) |
get_awesome_items | context-awesome items <target> | Fetch items from a known list + section, token-budgeted |
CLI Commands
The CLI (context-awesome) talks directly to the hosted backend. For the MCP server, use the separate context-awesome-mcp binary (see Installation — MCP Clients below).
context-awesome <command> [options]
Commands:
sections <query...> Find sections matching a query
search <query...> Search items (e.g., context-awesome search "postgres orm")
items <target> Fetch items from a list (by owner/repo or listId)
Globals:
--api-host <url> Backend API host (env: CONTEXT_AWESOME_API_HOST)
--api-key <key> API key (env: CONTEXT_AWESOME_API_KEY)
--json Emit raw JSON (for scripts)
Install the CLI
npm install -g context-awesome
context-awesome --help
context-awesome search "rate limiter"
context-awesome sections "graph databases"
Use the CLI without installing
npx context-awesome search "vector database"
Installation — MCP Clients
Remote Server (Recommended)
Context Awesome is available as a hosted MCP server. No installation required.
Install in Cursor
Go to: Settings → Cursor Settings → MCP → Add new global MCP server
{
"mcpServers": {
"context-awesome": {
"url": "https://www.context-awesome.com/api/mcp"
}
}
}
Install in Claude Code
claude mcp add --transport http context-awesome https://www.context-awesome.com/api/mcp
Install in Claude Desktop
Settings → Connectors → Add Custom Connector.
- Name:
Context Awesome - URL:
https://www.context-awesome.com/api/mcp
Install in Windsurf / VS Code / Zed / JetBrains / LM Studio / ...
Use the same URL (https://www.context-awesome.com/api/mcp) with each client's "add remote MCP" UI. See the dedicated sections below for exact snippets.
Local stdio (Claude Desktop, offline-capable)
{
"mcpServers": {
"context-awesome": {
"command": "npx",
"args": ["-y", "context-awesome-mcp", "serve", "--transport", "stdio"],
"env": {
"CONTEXT_AWESOME_API_HOST": "https://api.context-awesome.com"
}
}
}
}
Local HTTP transport (for custom integrations)
npx context-awesome-mcp serve --transport http --port 3001
# then point your client at http://localhost:3001/mcp
Local Development
git clone https://github.com/bh-rat/context-awesome.git
cd context-awesome
npm install
npm run build
# CLI
./build/cli.js search "graph databases"
# MCP server (stdio)
./build/index.js --transport stdio
# MCP Inspector
npm run inspector
Backend service
This MCP server and CLI connect to backend API service that handles the heavy lifting of awesome list processing.
The backend service will be open-sourced soon, enabling the community to contribute to and benefit from the complete context-awesome ecosystem.
Additional Installation Methods
Install in Cline
{
"mcpServers": {
"context-awesome": {
"url": "https://www.context-awesome.com/api/mcp"
}
}
}
Install in Zed
{
"context_servers": {
"context-awesome": {
"url": "https://www.context-awesome.com/api/mcp"
}
}
}
Install in Augment Code
- Click the hamburger menu
- Select Settings
- Navigate to Tools
- Click + Add MCP
- Enter URL:
https://www.context-awesome.com/api/mcp - Name: Context Awesome
Install in Roo Code
{
"mcpServers": {
"context-awesome": {
"type": "streamable-http",
"url": "https://www.context-awesome.com/api/mcp"
}
}
}
Install in Gemini CLI
{
"mcpServers": {
"context-awesome": {
"httpUrl": "https://www.context-awesome.com/api/mcp"
}
}
}
Install in Opencode
"mcp": {
"context-awesome": {
"type": "remote",
"url": "https://www.context-awesome.com/api/mcp",
"enabled": true
}
}
Install in JetBrains AI Assistant
- Go to
Settings->Tools->AI Assistant->Model Context Protocol (MCP) - Click
+ Add - Configure URL:
https://www.context-awesome.com/api/mcp - Click
OKandApply
Install in Kiro
- Navigate
Kiro>MCP Servers - Click
+ Add - Configure URL:
https://www.context-awesome.com/api/mcp - Click
Save
Install in Trae
{
"mcpServers": {
"context-awesome": {
"url": "https://www.context-awesome.com/api/mcp"
}
}
}
Install in Amazon Q Developer CLI
{
"mcpServers": {
"context-awesome": {
"url": "https://www.context-awesome.com/api/mcp"
}
}
}
Install in Warp
- Navigate
Settings>AI>Manage MCP servers - Click
+ Add - Configure URL:
https://www.context-awesome.com/api/mcp - Click
Save
Install in Copilot Coding Agent
{
"mcpServers": {
"context-awesome": {
"type": "http",
"url": "https://www.context-awesome.com/api/mcp",
"tools": ["find_awesome_section", "search_awesome_items", "get_awesome_items"]
}
}
}
Install in LM Studio
- Navigate to
Program>Install>Edit mcp.json - Add:
{
"mcpServers": {
"context-awesome": {
"url": "https://www.context-awesome.com/api/mcp"
}
}
}
Install in BoltAI
{
"mcpServers": {
"context-awesome": {
"url": "https://www.context-awesome.com/api/mcp"
}
}
}
Install in Perplexity Desktop
- Navigate
Perplexity>Settings - Select
Connectors - Click
Add Connector - Select
Advanced - Enter Name:
Context Awesome - Enter URL:
https://www.context-awesome.com/api/mcp
Install in Visual Studio 2022
{
"inputs": [],
"servers": {
"context-awesome": {
"type": "http",
"url": "https://www.context-awesome.com/api/mcp"
}
}
}
Install in Crush
{
"$schema": "https://charm.land/crush.json",
"mcp": {
"context-awesome": {
"type": "http",
"url": "https://www.context-awesome.com/api/mcp"
}
}
}
Install in Rovo Dev CLI
acli rovodev mcp
Then add:
{
"mcpServers": {
"context-awesome": {
"url": "https://www.context-awesome.com/api/mcp"
}
}
}
Install in Zencoder
- Go to Zencoder menu (...)
- Select Agent tools
- Click Add custom MCP
- Name:
Context Awesome - URL:
https://www.context-awesome.com/api/mcp
Install in Qodo Gen
- Open Qodo Gen chat panel
- Click Connect more tools
- Click + Add new MCP
- Add:
{
"mcpServers": {
"context-awesome": {
"url": "https://www.context-awesome.com/api/mcp"
}
}
}
License
MIT
Contributing
Contributions are welcome! Please:
- Fork the repository
- Create a feature branch
- Add tests for new functionality
- Ensure all tests pass
- Submit a pull request
Support
For issues and questions:
- GitHub Issues: https://github.com/bh-rat/context-awesome/issues
Attribution
This project uses data from over 8,500 awesome lists on GitHub. See ATTRIBUTION.md for a complete list of all repositories whose data is included.
Credits
Built with:
- Model Context Protocol SDK
- Awesome Lists
- Inspired by context7 MCP server patterns
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.