Browser Use
by browser-use
Lets AI do web tasks for you, such as filling forms, searching and extracting data, in the maker’s cloud browsers or on your computer.
Computer or cloud★ 120,087MITUpdated 16 days ago
Install
CrawlEyes MCP Server
Overview
Description from the maker (original English)
Web scraping & search for AI agents: extract, SearXNG search, rerank, robots compliance. No API key.
PopNx MCP’s three pillars
These three are never merged into one score: opinions, measurements and popularity are different things.
- User voiceiNo reviews yetReviews open together with accounts in a later step
- Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
- UsageiNot measured yet★ –
What you can do with it
We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.
- Connection: Runs on your computer
- Transport: stdio
- Category: Web & browsers
- You need to fill in: TAVILY_API_KEY
Install
Pick the AI app you use, then copy the settings.
Before you install
- Basic checks: 1 of 1 passed
- Runs on your computer with your user’s permissions (files, network, apps).
- You fill in yourself:
SEARXNG_URL,TAVILY_API_KEY
Values marked secret are keys to your account. Never share them or paste them into a chat.
You need
- PyPIPython’s uv installed (for the uvx command)
Which app do you use?
Pick your app above. We remember it, so next time the button says “Install in …”.
Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json
{
"mcpServers": {
"crawleyes": {
"command": "uvx",
"args": [
"crawleyes"
],
"env": {
"SEARXNG_URL": "your-value",
"TAVILY_API_KEY": "your-value"
}
}
}
}Run this in your terminal
claude mcp add crawleyes --env SEARXNG_URL=your-value --env TAVILY_API_KEY=your-value -- uvx crawleyesFill in the empty values before running it.
Or copy the settings yourself
Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"crawleyes": {
"command": "uvx",
"args": [
"crawleyes"
],
"env": {
"SEARXNG_URL": "your-value",
"TAVILY_API_KEY": "your-value"
}
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
Or copy the settings yourself
Paste into .vscode/mcp.json in your project
{
"servers": {
"crawleyes": {
"type": "stdio",
"command": "uvx",
"args": [
"crawleyes"
],
"env": {
"SEARXNG_URL": "your-value",
"TAVILY_API_KEY": "your-value"
}
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.
Your choice stays on this device in a cookie; no account needed.
Values you fill in
SEARXNG_URLenvOptional. Base URL of a SearXNG instance (e.g. http://localhost:8080). If unset, falls back to Tavily keyless.- Optional. Tavily API key for higher-rate search fallback. If unset, uses Tavily keyless (rate-limited).
These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.
What it can do
Runs on your computer; the tool list appears in your AI app after you install it.
Basic checksi
We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 6, 2026
- ✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account waiky-github, which owns the repo
- … Not checked yet
Still maintained
We have not read this repo from GitHub yet
- … Not checked yet
Has a licence
We have not read this repo from GitHub yet
- – Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
- – Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
- … Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints
Compared with others in Web & browsersi
- More stars than 0% of the other 876 servers in this category
- Published to the registry more recently than 34% of the other 876 servers in this category
- Basic checks equal to or better than 100% of the other 876 servers in this category
Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.
Common questions
Is CrawlEyes MCP Server safe?
It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.
What keys or codes does it need?
You need to fill in: TAVILY_API_KEY
Does it run on my computer or in the cloud?
It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).
Is it still maintained?
The GitHub repo last changed –, and the latest version (0.2.0) reached the registry 2 months ago.
Which apps can use it?
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.
What can I use instead?
In the same category: Browser Use, Chrome DevTools MCP, MCP Server Browser
Alternatives
Servers in the same category from other makers, sorted by stars.
Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
Chrome DevTools MCP
by ChromeDevTools
Lets AI use Chrome’s developer tools to inspect pages, find what makes them slow and spot errors. Made by the Chrome DevTools team.
Runs on your computer★ 54,821Apache-2.0Updated 15 days ago
Install
MCP Server Browser
by bytedance
MCP server for browser use access
Runs on your computer★ 39,550Apache-2.0Updated 14 months ago
Install
README from the maker
Open the README
Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.
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 .venvUse 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.comandHF_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 127.0.0.1POST /v2/scrape → { success, data: { markdown, metadata } }
GET /healthz → health check
from firecrawl import Firecrawlfc = 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-tavilyhermes 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 venvvenv/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
Only the beginning is shown; read the rest in the repo