一个极其高速的MCP（模型上下文协议）服务器，用于多引擎网页搜索，使用Rust编写。
概览
一个极其快速的多引擎网页搜索MCP服务器，用Rust编写。 Claude Code 内置的网页搜索在理想的网络条件下表现良好——但实际上，许多开发者会遇到不可靠或无法使用的环境：企业网络、受限区域、空屏设置，或连接断断续续。 在寻找变通方法时，我发现了open-webSearch，一个Node.js MCP服务器，可以通过多个引擎路由搜索查询。它很好地解决了这个问题。但我对Rust很感兴趣——而为了代理几个HTTP请求而启动一个Node.js运行时，感觉比必要的还要繁重。 所以我在 Rust 里重写了同样的想法：- — 单一自包含二进制，~8 MB - — Rust 异步输入输出，跨引擎并发输出 - — 内存使用极低 - — 通过 env var 实现 HTTP/SOCKS5 代理，适用于需要的网络 如果你的环境中Claude Code的搜索不起作用，这个就是为你准备的。 Ferris-Search 也是场景的良好基础。
概述（AI 翻译）
一个极速的多引擎网页搜索MCP服务器，用Rust编写。
为什么要做铁丝搜索？
Claude Code 内置的网页搜索在理想的网络条件下表现良好——但实际上，许多开发者会遇到不可靠或无法使用的环境：企业网络、受限区域、空屏设置，或连接断断续续。
在寻找变通方法时，我发现了open-webSearch，一个Node.js MCP服务器，可以通过多个引擎路由搜索查询。它很好地解决了这个问题。但我对Rust很感兴趣——而为了代理几个HTTP请求而启动一个Node.js运行时，感觉比必要的还要繁重。
所以我在Rust里重写了同样的想法：
- 无Node.js运行时 — 单一自包含二进制，~8 MB
- 延迟更低 — Rust异步输入输出，跨引擎同步扇出
- 更小的占用 — 内存占用可忽略不计
- 代理支持 — 通过env var支持HTTP/SOCKS5代理，适用于需要的网络
如果你的环境中Claude Code的搜索不起作用，这个就是为你准备的。
企业与内部使用
Ferris-Search 也是企业内部搜索场景的良好基础。
本页由 AI 翻译，技术细节请以原文为准。
README
以下为原文 README 节选，完整内容请查看原文。
ferris-search 🦀
A blazing-fast MCP server for multi-engine web search, written in Rust.
Why ferris-search?
Claude Code’s built-in web search works great in ideal network conditions — but in practice, many developers run into environments where it’s unreliable or unavailable: corporate networks, restricted regions, air-gapped setups, or simply spotty connectivity.
While looking for a workaround, I came across open-webSearch, a Node.js MCP server that routes search queries through multiple engines. It solved the problem well. But I have a thing for Rust — and spinning up a Node.js runtime just to proxy a few HTTP requests felt heavier than it needed to be.
So I rewrote the same idea in Rust:
- No Node.js runtime — single self-contained binary, ~8 MB
- Lower latency — Rust async I/O, concurrent fan-out across engines
- Smaller footprint — negligible memory usage
- Proxy support — HTTP/SOCKS5 proxy via env var, for networks that need it
If Claude Code’s search isn’t working in your environment, this is for you.
Enterprise & Internal Use
ferris-search is also a good foundation for enterprise internal search scenarios. Since it’s a standard MCP server written in Rust, you can fork it and add custom search engines that connect to your internal knowledge bases — Confluence, Notion, internal wikis, code repositories, or proprietary document stores.
Some ideas:
- Add an engine that searches your internal Elasticsearch or OpenSearch cluster
- Integrate with your company’s Confluence or GitLab search API
- Connect to a private RAG (Retrieval-Augmented Generation) service
- Route queries to different backends based on query language or topic
With Claude Code as the AI layer and ferris-search as the search backbone, your team gets a local AI coding assistant that can actually find and reference internal documentation — without sending anything to external search engines.
Features
- Multi-engine fan-out — search across multiple engines simultaneously with a single call
- 14 search engines — Bing, DuckDuckGo, Brave, Baidu, CSDN, Juejin, Exa, Firecrawl, Zhihu, LinuxDo, Jina, Tavily, GitHub (repo search), GitHub Code (code search)
- 7 MCP tools —
web_search+ 6 article/content fetchers - No API keys required for most engines (Brave, Exa, Firecrawl, Jina, and Tavily require API keys)
- Single binary — ~8 MB, no runtime dependencies
- Proxy support — HTTP/SOCKS5 proxy via env var
Quick Install
Linux / macOS (one-liner)
curl -fsSL https://raw.githubusercontent.com/lispking/ferris-search/main/install.sh | bash
Or from a local clone:
bash install.sh
The script will:
- Build and install the binary via
cargo install - Register the MCP server with Claude Code (
claude mcp add -s user) if the CLI is found - Install Claude Code skills for ferris-search
Windows (PowerShell)
安装
This server does not publish a one-line install command.
Open the repository installation guide 新手？先看 mcpskill.org 教程 →配置
{
"mcpServers": {
"ferris-search": {
"command": "/Users/<you>/.cargo/bin/ferris-search",
"env": {
"DEFAULT_SEARCH_ENGINE": "bing"
}
}
}
}