CrawlEyes MCP Server

Name: CrawlEyes MCP Server
Author: waiky-github

作者：waiky-github版本 0.2.0i★ –i在你的电脑上运行

注册名称io.github.waiky-github/CrawlEyes

安装

概览

作者提供的说明（英文原文）

Web scraping & search for AI agents: extract, SearXNG search, rerank, robots compliance. No API key.

PopNx MCP 的三大支柱

这三项永远不会合并成一个分数：意见、测量和人气是不同的东西。

用户评价i暂无评价评价功能将在后续阶段与账户功能一起开放
我们的基础检查i1 项中通过 1 项依据公开信息检查；尚未扫描代码
使用情况i尚未统计★ –

它能帮你做什么

我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。

连接方式: 在你的电脑上运行
传输方式: stdio
分类: 网页与浏览器
你需要填写：TAVILY_API_KEY

安装

选择你使用的 AI 应用，然后复制设置。

安装之前

基础检查：1 项中通过 1 项
在你的电脑上运行，拥有你的用户账户的权限（文件、网络、应用）。
需要你自己填写： SEARXNG_URL, TAVILY_API_KEY

标为机密的值是你账户的钥匙。千万不要分享给别人，也不要粘贴到聊天里。

你需要

PyPI已安装 Python 的 uv（用于 uvx 命令）

你用的是哪个应用？

ClaudeClaude CodeCursorVS CodeChatGPT

在上方选择你的应用。我们会记住它，下次按钮会显示“安装到 …”。

打开 Claude Desktop → Settings → Developer → Edit Config，把下面的内容粘贴到 claude_desktop_config.json

{
  "mcpServers": {
    "crawleyes": {
      "command": "uvx",
      "args": [
        "crawleyes"
      ],
      "env": {
        "SEARXNG_URL": "your-value",
        "TAVILY_API_KEY": "your-value"
      }
    }
  }
}

在终端中运行这条命令

claude mcp add crawleyes --env SEARXNG_URL=your-value --env TAVILY_API_KEY=your-value -- uvx crawleyes

运行前请先填写空着的值。

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json，或 ~/.cursor/mcp.json

{
  "mcpServers": {
    "crawleyes": {
      "command": "uvx",
      "args": [
        "crawleyes"
      ],
      "env": {
        "SEARXNG_URL": "your-value",
        "TAVILY_API_KEY": "your-value"
      }
    }
  }
}

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

{
  "servers": {
    "crawleyes": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "crawleyes"
      ],
      "env": {
        "SEARXNG_URL": "your-value",
        "TAVILY_API_KEY": "your-value"
      }
    }
  }
}

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

这个服务器在你的电脑上运行，而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。

你的选择通过 Cookie 保存在这台设备上，无需账户。

需要你填写的值

SEARXNG_URL env
Optional. Base URL of a SearXNG instance (e.g. http://localhost:8080). If unset, falls back to Tavily keyless.
TAVILY_API_KEY 机密 env
Optional. Tavily API key for higher-rate search fallback. If unset, uses Tavily keyless (rate-limited).

这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同，请以 README 为准。

它能做什么

它在你的电脑上运行；安装后，工具列表会显示在你的 AI 应用中。

基础检查i

我们检查的是任何人都能看到的信息；我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月6日

✓ 通过
作者与仓库一致
注册名称已通过 GitHub 账户 waiky-github 验证，该账户拥有此仓库
… 尚未检查
仍在维护
我们还没有从 GitHub 读取这个仓库
… 尚未检查
有许可证
我们还没有从 GitHub 读取这个仓库
– 不适用
端点有响应
它在你的电脑上运行，没有互联网端点
– 不适用
使用 OAuth 登录
在你的电脑上运行，改用本地设置
… 尚未检查
工具描述中没有隐藏指令
它在你的电脑上运行；我们只能读取互联网端点的工具列表

与“网页与浏览器”中的其他服务器比较i

星标数多于此分类中其他 875 个服务器的 0%
发布到注册中心的时间比此分类中其他 875 个服务器的 34% 更新
基础检查结果等于或优于此分类中其他 875 个服务器的 100%

与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时，星标数才计入。

常见问题

CrawlEyes MCP Server 安全吗？

它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前，请先看看这个服务器要求哪些权限，并使用只具备所需权限的密钥。

它需要哪些密钥或代码？

你需要填写：TAVILY_API_KEY

它在我的电脑上运行还是在云端？

它在你的电脑上运行。你的 AI 应用会在需要时启动它，数据不会经过作者的服务器（除非它自己调用在线服务）。

它还在维护吗？

GitHub 仓库最后更新于–，最新版本（0.2.0）于2个月前进入注册中心。

哪些应用可以使用它？

支持 MCP 的应用，例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Python (uv)。

有什么可以替代它？

同一分类中有：Browser Use, Chrome DevTools MCP, MCP Server Browser

替代选择

同一分类中其他作者的服务器，按星标数排序。

如何看卡片：★ GitHub 星标i三大支柱：评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i

Browser Use

作者：browser-use

Browser agents and stealth cloud browsers for AI: run web tasks, fill forms, extract data.

电脑或云端★ 120,087MIT16天前更新

安装

Chrome DevTools MCP

作者：ChromeDevTools

MCP server for Chrome DevTools

在你的电脑上运行★ 54,821Apache-2.015天前更新

安装

MCP Server Browser

作者：bytedance

MCP server for browser use access

在你的电脑上运行★ 39,550Apache-2.014个月前更新

安装

作者的 README

打开 README

按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。

CrawlEyes — Web Scraping & Search Toolkit for AI Agents

English | 中文

CrawlEyes gives AI agents reliable full-text extraction (web_extract) and robust search (web_search) backends — the "eyes" that let agents see and read the web. Built and tested against Hermes Agent.

Also ships as a standard MCP server, so any MCP client (Claude Desktop, Cursor, other agents) can reuse the same search + extraction capabilities.

Why CrawlEyes?

Most agent toolkits cover *one* slice of the pipeline. CrawlEyes is the rare all-in-one that you can actually run behind the Great Firewall without external accounts.

	Typical agent toolkit	CrawlEyes
🔍 Search	API key required, often blocked in CN	✅ SearXNG (self-hosted) + Tavily keyless fallback — zero config, zero key
📄 Extraction	Separate scraper, or Firecrawl SaaS	✅ Built-in Crawl4AI full-text extract, ~89% noise removal
🧠 Semantic rerank	Rarely included	✅ Local fastembed rerank — no torch, ~50MB model
🔌 MCP server	Often missing	✅ Standard MCP tools (`search` + `extract` + `deep_research` + `sitemap`), any client
🌐 China-friendly	Mostly English/GFW-blocked	✅ Tested on a real mainland China server (baidu + yandex)

Zero API keys. Zero external accounts. One command. CrawlEyes is the only toolkit in this space that combines search + extraction + semantic reranking + MCP in a single, China-friendly, self-hosted package.

Features

Capability	Where	Why it matters
Full-text extraction	`scripts/crawl4ai_cli.py`	Headless-browser scraping → clean Markdown; handles ~80% of JS/dynamic/UA-blocked pages
Content denoising (P1)	`crawl4ai_cli.py --noise-filter`	Prunes nav/ads/comments via Crawl4AI's `PruningContentFilter` — measured 24.6k→2.8k chars (~89% noise removed) on a typical article
Retry with backoff (P3)	`crawl4ai_cli.py --retry N`	Exponential backoff (1s/2s/4s) on transient failures
Browser session reuse (P4)	`crawl4ai_cli.py --session NAME`	Reuses the browser context across scrapes in one process — no cold-start per URL
Keyword-focused extraction	`crawl4ai_cli.py --bm25 KEYWORD`	Keeps only paragraphs relevant to a keyword (experimental — BM25 is English-centric; works best on English docs)
Search (primary)	SearXNG (self-hosted meta-search)	Privacy-friendly search aggregator
Search (fallback)	Tavily keyless API	Zero-config, no-key fallback when SearXNG is down/empty
Search orchestration	`plugins/searxng-tavily/`	Hermes plugin provider: SearXNG first → auto-fallback to Tavily keyless; three-state circuit breaker (3 fails → 60s cooldown → half-open) + shared SQLite cache (TTL 3600s)
Semantic reranking (P2)	`scripts/crawl_search_standalone.py`	Local embedding rerank of search results with `fastembed` + `BAAI/bge-small-zh-v1.5` (512-dim, no torch dependency, ~50MB, cached) — puts relevant results first. Measured: crawler-relevant items 0.817/0.732 float to top, irrelevant 0.302/0.139 sink
MCP server (P5)	`scripts/mcp_crawl_server.py`	Exposes `search` + `extract` + `deep_research` + `sitemap` as standard MCP tools (stdio default, or streamable-http for remote clients). Works in any MCP client, no Hermes dependency. Extracted content is sanitized against prompt-injection (strips invisible chars + prompt-hijack lines). Unified rate limiting + exponential backoff guard every tool (sliding window, per-tool cost) so concurrent agent calls can't hammer downstream services
Sitemap discovery (P1)	`crawleyes/sitemap.py`	`sitemap(origin)` → parses `sitemap.xml` (plain / gzip / index-recursion) with `robots.txt` fallback, returns a deduped URL map. Zero-key way to discover a site's URL surface for whole-site fetch or deep-research seeding
Multi-format extract (P0)	`extract(..., format=)`	`markdown` (default) / `fit` (denoised) / `raw` (unfiltered) / `markdown_with_citations` — pick the level of cleanup you need
RAG-ready interfaces	`crawleyes/rag.py`	One-liners `markdown(url)` / `search_markdown(query)` → clean, sanitized, LLM-ready Markdown for RAG corpora
Deep research	`crawleyes/deep_research.py`	`deep_research(topic)` → decomposes topic into sub-questions → searches → extracts → synthesizes a cited Markdown report. Optional LLM (any OpenAI-compatible endpoint); degrades to evidence-aggregate mode without one
Verification	`scripts/`	Clean subprocess scripts to verify each backend end-to-end per Hermes profile

Project layout

plugins/searxng-tavily/   Hermes web-search provider plugin (SearXNG → Tavily keyless fallback)

three-state circuit breaker + shared SQLite cache

scripts/ crawl4ai_cli.py Universal scraping CLI (URL → Markdown), with denoise/retry/session/BM25 crawl_search_standalone.py Standalone search (SearXNG → Tavily) + optional semantic rerank. No Hermes dependency — usable anywhere, powers the MCP server. mcp_crawl_server.py Standard MCP server exposing search + extract + deep_research + sitemap (stdio) single_env_check.py Verify crawl4ai provider registered+available+extracts (one profile) verify_searxng_tavily.py Verify searxng-tavily provider: normal path + forced fallback agent_link_check.py Verify full agent tool chain: web_search_tool dispatch + logs

Quick start

1. Install Crawl4AI (China-friendly mirrors)

python3 -m venv .venv

Use Tsinghua PyPI mirror for speed (or any mirror you prefer)

.venv/bin/pip install -i https://pypi.tuna.tsinghua.edu.cn/simple crawl4ai

Playwright browser kernel — use npmmirror binary mirror if cdn.playwright.dev is blocked

PLAYWRIGHT_DOWNLOAD_HOST=https://registry.npmmirror.com/-/binary/playwright \ .venv/bin/python -m playwright install chromium .venv/bin/crawl4ai-setup

2. Scrape a page

.venv/bin/python scripts/crawl4ai_cli.py https://example.com          # stdout Markdown

.venv/bin/python scripts/crawl4ai_cli.py https://example.com -o out.md # to file .venv/bin/python scripts/crawl4ai_cli.py URL --text --max-words 5000 # plain text, truncated

Multi-format extraction (markdown|fit|raw|markdown_with_citations)

.venv/bin/python scripts/crawl4ai_cli.py URL --format raw # unfiltered source markdown .venv/bin/python scripts/crawl4ai_cli.py URL --format markdown_with_citations # + source URLs

Respect robots.txt (opt-in, default off)

.venv/bin/python scripts/crawl4ai_cli.py URL --respect-robots

Denoise nav/ads + retry 3x + reuse session across scrapes

.venv/bin/python scripts/crawl4ai_cli.py URL --noise-filter --retry 3 --session s1

3. Use the search + rerank (standalone, no Hermes)

# Optional: local semantic rerank of results (fastembed + bge-small-zh, auto-downloaded)

.venv/bin/pip install -i https://pypi.tuna.tsinghua.edu.cn/simple fastembed

SearXNG first, Tavily keyless fallback, then rerank

SEARXNG_URL=https://your-searxng .venv/bin/python -c " import sys; sys.path.insert(0, 'scripts') from crawl_search_standalone import CrawlSearch r = CrawlSearch(rerank=True).search('your query') print(r'data')"

China-network note: the embedding model downloads from HuggingFace, which is blocked on mainland networks. Set HF_ENDPOINT=https://hf-mirror.com and HF_HUB_DISABLE_XET=1 (hf-mirror doesn't support the xet protocol and returns 401 without this).

4. Run as an MCP server (any client)

# Any MCP client can connect via stdio (default):

.venv/bin/python scripts/mcp_crawl_server.py

Exposes tools:

search(query, limit) - SearXNG → Tavily keyless, rerank, retry+rate-limit

extract(url, max_words, format) - markdown|fit|raw|markdown_with_citations

deep_research(topic, num_questions) - multi-round cited report

sitemap(origin, max_urls) - URL map from sitemap.xml / robots.txt

Or serve over HTTP (streamable-http) for remote clients:

.venv/bin/python -m crawleyes.mcp_crawl_server --transport http --port 8765 --host 127.0.0.1

→ clients connect to http://127.0.0.1:8765/mcp

(host/port configurable; default 127.0.0.1:8765)

For Hermes specifically, add to config.yaml:

mcp_servers:

crawl: command: "/path/to/crawl/.venv/bin/python" args: ["/path/to/crawl/scripts/mcp_crawl_server.py"] timeout: 90 connect_timeout: 60

4b. Firecrawl-compatible `/scrape` endpoint

Already using Firecrawl's Python SDK? Point it at CrawlEyes and keep your code:

.venv/bin/python -m crawleyes.firecrawl_api --port 8899 --host 127.0.0.1

POST /v2/scrape → { success, data: { markdown, metadata } }

GET /healthz → health check

from firecrawl import Firecrawl

fc = Firecrawl(api_url="http://127.0.0.1:8899", api_key="ignored") doc = fc.scrape(url="https://example.com") # → { markdown, metadata }

This is a pragmatic subset of the Firecrawl API — the core /scrape contract (success + data.markdown + data.metadata), backed by CrawlEyes' own extraction engine. It does not implement Firecrawl's async /crawl queue, /search, or /map — see the design notes for the rationale.

5. Install the search plugin (Hermes)

Copy plugins/searxng-tavily/ into a Hermes plugins dir, then:

hermes plugins enable web/searxng-tavily

hermes config set web.search_backend searxng-tavily

Set SEARXNG_URL in your Hermes profile .env to point at your SearXNG instance. If unset or unreachable, the provider automatically falls back to the Tavily keyless API (no API key required).

Note: the plugin only takes effect for newly started agent sessions.

6. Verify

# Requires the Hermes source tree + its venv

venv/bin/python scripts/verify_searxng_tavily.py $HERMES_HOME venv/bin/python scripts/agent_link_check.py $HERMES_HOME

Design notes

Layered composition: no single tool covers everything. Crawl4AI handles extraction; SearXNG + Tavily cover search; each layer has a tested fallback.
Tavily keyless works with zero configuration and no account — a cheap resilience net for the whole search path.
Circuit breaker is SearXNG-only: a Tavily fallback success does *not* reset the breaker (otherwise it would never trip). record_success() is only called when SearXNG itself succeeds.
Shared SQLite cache lives in the *real* user home (via pwd.getpwuid, not $HOME — which Hermes profiles override), so all profiles share one cache. WAL + 5s timeout + try/except degrade-to-no-cache under concurrency.
Semantic rerank is cheap: fastembed (ONNX) avoids the ~2GB torch dependency; model loads in ~0.6s once cached, embeddings in ~50ms.
MCP server is standalone: it does *not* import Hermes internals, so it runs on any Python 3.12 env and serves any MCP client.
MCP transport is dual: stdio (default, standard MCP clients) or streamable-http (--transport http), so a single codebase serves bot

只显示了开头部分；其余内容请在仓库中阅读