ORZ MCP
This server gives AI assistants two web tools: searching the internet and fetching web page content.
web_search — Searches the web via multiple engines (Brave, Sogou, DuckDuckGo) in parallel, deduplicating results and filtering ads; takes
query(required) andnum_results(default 8), returns{url, title, summary}[].web_fetch — Retrieves a URL's content with a 10-second timeout; takes
url(required),max_char_size(default 50000), andsimplify(default true, strips useless HTML and converts to Markdown); returns plain text/Markdown, errors clearly on non-text types like PDFs or images, and (in the HTTP version) blocks SSRF to loopback/private/link-local addresses.
Enables web search capabilities by querying the Brave search engine to retrieve results that are automatically deduplicated and filtered for ads.
Provides web search functionality by querying DuckDuckGo to retrieve results that are automatically deduplicated and filtered for ads.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@ORZ MCP search for the latest news on Claude 3.5 Sonnet and summarize it"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
ORZ MCP
一个提供 web_search 和 web_fetch 能力的 MCP (Model Context Protocol) 服务器。
让你的 AI 助手(Claude、Cursor、OpenCode 等)能够搜索互联网和抓取网页内容。
功能
web_search
通过 DuckDuckGo 搜索网页,自动去重、过滤广告。
入参:
query(搜索关键词)、num_results(返回数量,默认 8)返回:
{ url, title, summary }[]
web_fetch
抓取指定 URL 的网页内容,默认简化为 Markdown 格式。
入参:
url、max_char_size(最大字符数,默认 50000)、simplify(是否简化,默认 true)返回: 纯文本字符串(Markdown 格式)
内置 10 秒超时(覆盖重定向、
<meta refresh>跳转和读取正文)支持表格(GFM)、保留代码块换行;PDF、图片等非文本类型会明确报错,不返回乱码
Streamable HTTP 版内置 SSRF 防护:拒绝回环、内网、链路本地(如云元数据
169.254.169.254)等地址,每次重定向都会重新校验
Related MCP server: websearch-skill
两种使用方式(二选一)
ORZ MCP 提供 stdio 和 Streamable HTTP 两种 MCP 传输协议的实现,功能完全一致,根据你的需求选择其中一种即可。
stdio | Streamable HTTP | |
运行方式 | 通过 npx 本地启动 | 远程 HTTP 服务(Netlify Functions) |
适用场景 | 本地运行,支持代理 | 开箱即用,支持云端部署、住宅代理 |
代理支持 | 支持 | 支持环境变量 |
依赖 | Node.js >= 18 | 无 |
方式一:stdio(通过 npx 本地运行)
无需安装,直接通过 npx 运行。
在你的 MCP 客户端配置中添加:
1. 配置住宅代理(或科学上网代理):
{
"mcpServers": {
"orz": {
"type": "stdio",
"command": "npx",
"args": ["-y", "orz-mcp", "--proxy", "http://user:pass@proxy-host:port"]
}
}
}方式二:Streamable HTTP(远程连接)
无需本地安装任何东西,直接连接部署在 Netlify 上的远程服务。
{
"mcpServers": {
"orz": {
"type": "http",
"url": "https://<your-netlify-domain>/mcp"
}
}
}如果你的 MCP 客户端不支持直接 URL 连接,可以通过 mcp-remote 桥接:
{
"mcpServers": {
"orz": {
"command": "npx",
"args": ["mcp-remote@next", "https://<your-netlify-domain>/mcp"]
}
}
}配置文件位置
不同的 MCP 客户端配置文件位置不同:
Claude Desktop:
~/Library/Application Support/Claude/claude_desktop_config.json(macOS)Cursor: Settings > MCP Servers
OpenCode:
.opencode/config.json或通过/mcp命令添加
项目结构
orz-mcp/
├── stdio/ # stdio 传输协议 (npm 包)
│ ├── client.mjs # 入口文件
│ └── package.json # npm 发布配置(依赖: mcp sdk, turndown, undici)
├── streamable-http/ # Streamable HTTP 传输协议 (Netlify Functions)
│ ├── netlify/
│ │ ├── mcp-server/
│ │ │ └── index.ts # MCP Server 定义(工具注册与业务逻辑)
│ │ └── functions/
│ │ └── hono-mcp-server.ts # Hono HTTP handler (Netlify Function)
│ ├── public/
│ │ └── index.html # 静态首页
│ ├── netlify.toml # Netlify 构建配置
│ └── package.json # 服务端依赖(依赖: mcp sdk, hono, zod, turndown)
└── README.md开发
stdio
cd stdio
npm install
node client.mjs
node client.mjs --proxy http://127.0.0.1:7890
node client.mjs --help
# 用 MCP Inspector 调试
npx @modelcontextprotocol/inspector node client.mjsStreamable HTTP(本地调试)
cd streamable-http
npm install
# 启动本地开发服务器(需要 Netlify CLI)
netlify dev
# 用 MCP Inspector 测试(在 UI 中选择 Streamable HTTP,填入 URL)
npx @modelcontextprotocol/inspector --url http://localhost:8888/mcp部署到 Netlify
cd streamable-http
# 安装 Netlify CLI
npm install -g netlify-cli
# 登录
netlify login
# 初始化并关联站点
netlify init
# 部署
netlify deploy --prod或者通过 GitHub 连接 Netlify,push 到 main 分支自动部署。
环境变量
变量 | 说明 |
| 出站代理(如住宅 IP 代理)。配置后域名由代理解析,SSRF 防护只校验 IP 字面量和 |
| 逗号分隔的主机名/IP 白名单,允许 |
License
MIT
Available Tools
2 toolsweb_fetchA
Fetch a web page and return its content. When simplify is enabled (default), removes useless HTML tags and converts to Markdown. Has a 10-second timeout.
Inputs: url (required), max_char_size, simplify.
web_searchA
Search the web using multiple search engines (Brave, Sogou, DuckDuckGo) simultaneously. Results are deduplicated and ads are filtered out. Returns an array of search results with url, title, and summary.
Inputs: query (required), num_results.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 2 tools
The two tools have clearly distinct purposes: web_fetch retrieves and processes the content of a specific URL, while web_search queries multiple search engines to find relevant web pages. There is no overlap in functionality, making it easy for an agent to choose the right tool for the task.
Both tools follow a consistent 'web_' prefix and snake_case naming pattern (web_fetch and web_search). The naming is predictable and aligns with their web-related functions, providing clear and uniform conventions.
With only two tools, the server feels thin for a web-related domain. While the tools cover fetching and searching, there are obvious gaps such as navigation, form handling, or API interactions that could enhance completeness, making the count insufficient for robust web operations.
The tool set is severely incomplete for web-related tasks. It lacks essential operations like navigating between pages, handling cookies or sessions, interacting with forms, or accessing APIs. This will likely cause agent failures when trying to perform common web automation or data extraction workflows.
Related MCP Connectors
Web search, fetch, extract, and research for AI agents. Markdown output + AI-synthesized answers.
Web search, URL content extraction to Markdown, site mapping, and recursive web crawler.
Web search, Google SERP, page extraction to markdown and cited answers for AI agents.
Search the web, fetch any URL and map a site as clean, LLM-ready Markdown.
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