Context Awesome

io.github.bh-rat由 GitHub 帳號 bh-rat 發布v0.1.1更新於 Oct 24, 2026

MCP server for accessing curated awesome list documentation

發布方自己的服務:Awesome Tools

已驗證Streamable HTTP可網頁執行網頁搜尋與抓取知識與記憶

概覽

AI 產生的概覽

搜尋 GitHub 上社群精選的 awesome 清單,回傳符合的章節與資源項目,供助理引用。

可以做什麼
  • 依查詢在數千個 awesome 清單中找出符合的章節與分類
  • 從已知清單或章節取得項目(工具、函式庫、資源),並受 token 上限限制
  • 當使用者提供 GitHub 儲存庫或清單 ID 時,直接依該目標查詢
  • 相同操作也以命令列工具形式提供,方便腳本與 CI 使用
適用情境
適合希望助理推薦經過篩選的優質資源、而非依賴一般網頁搜尋的情境,例如深度研究、學習資源彙整或參考資料蒐集。
執行需求
可使用託管的遠端端點(無需安裝),或以 npx 從 context-awesome-mcp 套件啟動本機程序(需要 Node.js)。本機模式可設定 CONTEXT_AWESOME_API_HOST;命令列工具另支援 CONTEXT_AWESOME_API_KEY。需要能連線至託管後端。
安裝前請注意
查詢內容會傳送到第三方託管後端,搜尋詞會離開本機。後端尚未開源。命令列工具文件提到可透過 CONTEXT_AWESOME_API_KEY 提供 API 金鑰,應視為機密資訊。這些工具僅讀取資料。

安裝

在 SourceWeft 中

  1. 開啟 儀表板中的 Context Awesome,將其新增到工作區。
  2. 為需要使用其工具的對話啟用該服務。

Web executable,透過 Streamable HTTP。 遠端服務在工作區中設定後即可從網頁執行環境執行。

交給代理程式安裝

把這段提示詞貼上到 Claude Code、Codex、Cursor 等代理程式,或 SourceWeft 對話中。代理程式會讀取該伺服器的安裝指南,先向你展示發布者、端點和登入方式,經你同意後再把它新增到你的客戶端。金鑰不會寫進任何命令。

閱讀 https://sourceweft.com/mcp/io.github.bh-rat%2Fcontext-awesome/install.md,並按其中說明把這個 MCP 伺服器接入我的客戶端。修改任何設定之前,先告訴我它的發布者、端點和登入方式,等我確認。不要把 API 金鑰寫進命令或我會提交的檔案;問我金鑰應從哪裡取得。

檢視代理程式所依據的安裝指南

其他 MCP 客戶端

把它新增到你客戶端的 mcpServers 設定中。

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

README

context-awesome : awesome references for your agents [Awesome]

[MCP Server]

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 :

  1. Knowledge worker agents to get the most relevant references for their work
  2. The source for the best learning resources
  3. Deep research can quickly gather a lot of high quality resources for any topic.
  4. Search agents

https://github.com/user-attachments/assets/babab991-e4ff-4433-bdb7-eb7032e9cd11

Two Ways to Use Context Awesome

ModeInstallGood for
MCP Serverpoint your agent at the hosted URL or spawn context-awesome-mcpClaude Desktop, Cursor, Windsurf, VS Code — agents that natively speak MCP
CLInpm install -g context-awesomeScripts, 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.

ToolCLI equivalentWhat it does
find_awesome_sectioncontext-awesome sections <query...>Discover sections/categories across awesome lists matching a query
search_awesome_itemscontext-awesome search <query...>Full-text search across individual items (tools/libraries/resources)
get_awesome_itemscontext-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

bash
npm install -g context-awesomecontext-awesome --helpcontext-awesome search "rate limiter"context-awesome sections "graph databases"

Use the CLI without installing

bash
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

json
{  "mcpServers": {    "context-awesome": {      "url": "https://www.context-awesome.com/api/mcp"    }  }}
Install in Claude Code
sh
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)

json
{  "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)

bash
npx context-awesome-mcp serve --transport http --port 3001# then point your client at http://localhost:3001/mcp

Local Development

bash
git clone https://github.com/bh-rat/context-awesome.gitcd context-awesomenpm installnpm run build
# CLI./build/cli.js search "graph databases"
# MCP server (stdio)./build/index.js --transport stdio
# MCP Inspectornpm 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
json
{  "mcpServers": {    "context-awesome": {      "url": "https://www.context-awesome.com/api/mcp"    }  }}
Install in Zed
json
{  "context_servers": {    "context-awesome": {      "url": "https://www.context-awesome.com/api/mcp"    }  }}
Install in Augment Code
  1. Click the hamburger menu
  2. Select Settings
  3. Navigate to Tools
  4. Click + Add MCP
  5. Enter URL: https://www.context-awesome.com/api/mcp
  6. Name: Context Awesome
Install in Roo Code
json
{  "mcpServers": {    "context-awesome": {      "type": "streamable-http",      "url": "https://www.context-awesome.com/api/mcp"    }  }}
Install in Gemini CLI
json
{  "mcpServers": {    "context-awesome": {      "httpUrl": "https://www.context-awesome.com/api/mcp"    }  }}
Install in Opencode
json
"mcp": {  "context-awesome": {    "type": "remote",    "url": "https://www.context-awesome.com/api/mcp",    "enabled": true  }}
Install in JetBrains AI Assistant
  1. Go to Settings -> Tools -> AI Assistant -> Model Context Protocol (MCP)
  2. Click + Add
  3. Configure URL: https://www.context-awesome.com/api/mcp
  4. Click OK and Apply
Install in Kiro
  1. Navigate Kiro > MCP Servers
  2. Click + Add
  3. Configure URL: https://www.context-awesome.com/api/mcp
  4. Click Save
Install in Trae
json
{  "mcpServers": {    "context-awesome": {      "url": "https://www.context-awesome.com/api/mcp"    }  }}
Install in Amazon Q Developer CLI
json
{  "mcpServers": {    "context-awesome": {      "url": "https://www.context-awesome.com/api/mcp"    }  }}
Install in Warp
  1. Navigate Settings > AI > Manage MCP servers
  2. Click + Add
  3. Configure URL: https://www.context-awesome.com/api/mcp
  4. Click Save
Install in Copilot Coding Agent
json
{  "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
  1. Navigate to Program > Install > Edit mcp.json
  2. Add:
json
{  "mcpServers": {    "context-awesome": {      "url": "https://www.context-awesome.com/api/mcp"    }  }}
Install in BoltAI
json
{  "mcpServers": {    "context-awesome": {      "url": "https://www.context-awesome.com/api/mcp"    }  }}
Install in Perplexity Desktop
  1. Navigate Perplexity > Settings
  2. Select Connectors
  3. Click Add Connector
  4. Select Advanced
  5. Enter Name: Context Awesome
  6. Enter URL: https://www.context-awesome.com/api/mcp
Install in Visual Studio 2022
json
{  "inputs": [],  "servers": {    "context-awesome": {      "type": "http",      "url": "https://www.context-awesome.com/api/mcp"    }  }}
Install in Crush
json
{  "$schema": "https://charm.land/crush.json",  "mcp": {    "context-awesome": {      "type": "http",      "url": "https://www.context-awesome.com/api/mcp"    }  }}
Install in Rovo Dev CLI
bash
acli rovodev mcp

Then add:

json
{  "mcpServers": {    "context-awesome": {      "url": "https://www.context-awesome.com/api/mcp"    }  }}
Install in Zencoder
  1. Go to Zencoder menu (...)
  2. Select Agent tools
  3. Click Add custom MCP
  4. Name: Context Awesome
  5. URL: https://www.context-awesome.com/api/mcp
Install in Qodo Gen
  1. Open Qodo Gen chat panel
  2. Click Connect more tools
  3. Click + Add new MCP
  4. Add:
json
{  "mcpServers": {    "context-awesome": {      "url": "https://www.context-awesome.com/api/mcp"    }  }}

License

MIT

Contributing

Contributions are welcome! Please:

  1. Fork the repository
  2. Create a feature branch
  3. Add tests for new functionality
  4. Ensure all tests pass
  5. Submit a pull request

Support

For issues and questions:

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:

來源:README.md,提交 7ed2de1

工具

2

find_awesome_section

Discovers sections/categories across awesome lists matching a search query and returns matching sections from awesome lists. You MUST call this function before 'get_awesome_items' to discover available sections UNLESS the user explicitly provides a githubRepo or listId. Selection Process: 1. Analyze the query to understand what type of resources the user is looking for 2. Return the most relevant matches based on: - Name similarity to the query and the awesome lists section - Category/section relevance of the awesome lists - Number of items in the section - Confidence score Response Format: - Returns matching sections of the awesome lists with metadata - Includes repository information, item counts, and confidence score - Use the githubRepo or listId with relevant sections from results for get_awesome_items For ambiguous queries, multiple relevant sections will be returned for the user to choose from.

輸入參數limitquery必填confidence

get_awesome_items

Retrieves items from a specific awesome list or section with token limiting. You must call 'find_awesome_section' first to discover available sections, UNLESS the user explicitly provides a githubRepo or listId.

輸入參數listIdoffsettokenssectiongithubReposubcategory

版本歷史

1
  1. v0.1.1最新Sep 16, 2026

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