crawl4ai-mcp

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A minimal MCP server for agent-friendly web extraction and search.

Two tools: fetch real pages with Playwright + Crawl4AI, or search across 7 engines with automatic fallback.

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---

Quick entry

Audience	Read this
Human developer	README.zh-CN.md / README.md
Living in the AI era, delegating your remaining sanity to an agent	README_AGENT.md

At a glance

Item	Reality in this repo
MCP tools	2 tools: fetch_urls + search_web
Single-page fetch	urls: ["https://example.com"]
Web search	search_web(query="...", engine="auto") — 7 engines, auto fallback
Search engines	DuckDuckGo · Bing · Google · Yandex · Sogou · 360Search · Baidu
Output	title + content + links + blocked + llm_used/llm_error
Non-LLM mode	First-class, default, usable without any model
LLM mode	Off by default. Enabled only with use_llm=true + optional llm_instruction
Fallback	Missing/failed LLM call automatically falls back to non-LLM result
Anti-bot realism	proxy / cookies / persistent profile / randomized browser behavior
License	AGPL-3.0-or-later

---

How it works

Fetch flow:

---
config:
  theme: base
  themeVariables:
    primaryColor: "#FAF9F5"
    primaryTextColor: "#1A1A1A"
    primaryBorderColor: "#D97757"
    lineColor: "#8B5E3C"
    secondaryColor: "#F5F1E8"
    tertiaryColor: "#FAF9F5"
    fontFamily: "ui-sans-serif, system-ui, -apple-system, sans-serif"
    fontSize: "14px"
---
flowchart LR
    A[URL list] --> B[Playwright + Crawl4AI]
    B --> C{Fast path enough?}
    C -- Yes --> D[Markdown / HTML]
    C -- No --> E[Stronger fallback]
    E --> D
    D --> F{use_llm?}
    F -- No --> G[Return result]
    F -- Yes --> H[OpenAI-compatible cleanup]
    H --> I{LLM success?}
    I -- Yes --> J[Return enhanced result]
    I -- No --> G

Search flow:

---
config:
  theme: base
  themeVariables:
    primaryColor: "#FAF9F5"
    primaryTextColor: "#1A1A1A"
    primaryBorderColor: "#D97757"
    lineColor: "#8B5E3C"
    secondaryColor: "#F5F1E8"
    tertiaryColor: "#FAF9F5"
    fontFamily: "ui-sans-serif, system-ui, -apple-system, sans-serif"
    fontSize: "14px"
---
flowchart LR
    A[query + engine] --> B{engine=auto?}
    B -- Yes --> C[Detect language]
    C --> D[Build engine plan]
    B -- No --> E[Use specified engine]
    D --> F[Try engines in order]
    E --> F
    F --> G{Results?}
    G -- Yes --> H[Aggregate + deduplicate]
    G -- No, next engine --> F
    H --> I[Return results]

---

Installation

Quick install (recommended)

Step 1: Create a virtual environment

# macOS/Linux - using system Python 3 (3.10-3.13)
python3 -m venv crawl4ai
source crawl4ai/bin/activate

# Windows
python -m venv crawl4ai
crawl4ai\Scripts\activate

Step 2: Install

pip install --upgrade pip
pip install crawl4agent
playwright install chromium

Alternative methods

If python3 is too old (3.9 or below):

# Use specific Python version (3.10, 3.11, 3.12, or 3.13)
python3.12 -m venv crawl4ai
source crawl4ai/bin/activate
pip install crawl4agent

Using conda:

conda create -n crawl4ai python=3.12
conda activate crawl4ai
pip install crawl4agent
playwright install chromium

Using pipx (global command):

pipx install crawl4agent
crawl4ai-mcp --help

Troubleshooting

Problem: "pip install" uses Python 2.7

# macOS: use python3 explicitly
python3 -m pip install crawl4agent

# Or check which pip you're using
which pip
pip --version

Problem: "No matching distribution found for crawl4agent"

• Check Python version: python3 --version (must be 3.10-3.13)
• Upgrade pip: python3 -m pip install --upgrade pip

Problem: "playwright install" fails

• Use mirror (China): export PLAYWRIGHT_DOWNLOAD_HOST=https://npmmirror.com/mirrors/playwright/
• Then: python3 -m playwright install chromium

---

Why this project exists

Most generic “web fetch” tools either fail on JS-heavy pages or return too much boilerplate. This project focuses on four things:

• Non-LLM quality first: usable even with zero model config
• Minimal MCP surface: easier for agents, easier to maintain
• Pragmatic anti-bot workflow: proxy / cookies / persistent profile are first-class
• Golden regression review: full markdown outputs can be saved and inspected page by page

---

Core capabilities

Non-LLM mode

Capability	Actual behavior
Rendering	Real browser rendering via Playwright
Extraction	Crawl4AI markdown/html extraction
Fallback	Fast path → stronger path when content is too thin
Cleanup	Remove obvious noise, compress blanks, strip data-image placeholders
Site tuning	Medium / Claude Docs / GitHub and other mainstream sites
ChatGPT shared links	Full conversation extraction from chatgpt.com/share/... URLs
Video transcripts	YouTube / Bilibili URLs prefer subtitle extraction via yt-dlp, then fall back to webpage extraction
Block detection	blocked=true for likely verification/interstitial output
Batch control	Bounded concurrency via concurrency

Optional LLM mode

Input	Meaning
use_llm=true	Turn on post-cleanup with an OpenAI-compatible model
llm_instruction	Tell the model what to keep / remove

Important reality check:

• With llm_instruction, the prompt is constraint-heavy and biased toward preserving original lines.
• Without llm_instruction, the model does a more generic “clean readable markdown” pass.
• If the LLM call fails for any reason, the tool returns the original non-LLM extraction plus llm_used=false and llm_error.

---

MCP Tools

fetch_urls

{
  "urls": ["https://a.com", "https://b.com"],
  "format": "markdown",
  "max_chars": 200000,
  "concurrency": 3,
  "use_llm": false,
  "llm_instruction": "keep only the tutorial body and in-body references"
}

Use a single-element list if you only need one page.

For supported video URLs (youtube.com, youtu.be, bilibili.com, b23.tv), fetch_urls prefers transcript extraction and returns readable markdown built from subtitles when available.

Return shape

Field	Meaning
url	Original URL
final_url	Final resolved URL after redirects
title	Extracted title
content	Markdown or HTML
content_format	markdown or html
links	Normalized extracted links
video_metadata	Present for supported video transcript extraction results
blocked	Likely anti-bot / verification / denied result
llm_used	Whether LLM enhancement was actually applied
llm_error	Why the LLM step degraded

search_web

{
  "query": "crawl4ai web scraping",
  "engine": "auto",
  "max_results": 10,
  "lang": ""
}

Parameter	Default	Description
query	(required)	Search query string
engine	auto	Engine to use: auto, google, bing, duckduckgo, baidu
max_results	10	Maximum number of results
lang	""	Language hint (e.g. en, zh-CN)

When engine="auto", the server tries engines in fallback order: DuckDuckGo → Bing → Google → Baidu. The first engine that returns results wins.

Search return shape

Field	Meaning
engine	Which engine actually returned results
query	Original query
results	List of {title, url, snippet}
total	Number of results
fallback_engines_tried	Engines that failed before the successful one

---

Anti-bot realism

The server already includes randomized browser behavior in code:

Mechanism	Actual status
Random viewport	Yes
Random user agent mode	Yes, when explicit UA is not provided
Delay jitter	Yes
override_navigator	Yes
simulate_user	Yes, in stronger fallback mode
Proxy / cookies / persistent profile	Supported via env vars
Cloudflare bypass	Enhanced browser fingerprinting + configurable wait strategies

Note: For overseas websites (Medium, ProductHunt, etc.), using a proxy is recommended. The server supports HTTP/HTTPS/SOCKS5 proxies via CRAWL4AI_MCP_PROXY environment variable.

Proxy input formats

CRAWL4AI_MCP_PROXY accepts all of these:

Input	Interpreted as
http://127.0.0.1:7890	HTTP proxy
https://127.0.0.1:7890	HTTPS proxy
socks5://127.0.0.1:7890	SOCKS5 proxy
socket5://127.0.0.1:7890	Auto-normalized to socks5://...
127.0.0.1:7890	Auto-normalized to http://127.0.0.1:7890
7890	Auto-normalized to http://127.0.0.1:7890

That means the README should not claim “perfect stealth”, but it can honestly claim human-like randomization and practical anti-bot knobs.

---

Quickstart

Conda

conda env create -f environment.yml
conda activate crawl4ai-mcp
python -m playwright install
crawl4ai-mcp

venv

python -m venv .venv
source .venv/bin/activate
python -m pip install -U pip
python -m pip install -e '.[dev]'
python -m playwright install
crawl4ai-mcp

---

MCP server config example

{
  "mcpServers": {
    "crawl4ai": {
      "command": "crawl4ai-mcp",
      "env": {
        "CRAWL4AI_MCP_HEADLESS": "true",
        "CRAWL4AI_MCP_PROXY": "127.0.0.1:7890",
        "CRAWL4AI_MCP_NAVIGATION_TIMEOUT_MS": "30000",
        "CRAWL4AI_MCP_WAIT_UNTIL": "load",

        "OPENAI_BASE_URL": "https://your-openai-compatible-host",
        "OPENAI_API_KEY": "your-api-key",
        "OPENAI_MODEL": "your-model-name"
      }
    }
  }
}

LLM-related env vars are optional. use_llm is still off by default at call time. If any LLM env is missing, invalid, or the model call fails, the server automatically falls back to non-LLM extraction.

---

Runtime configuration

Env var	Purpose
CRAWL4AI_MCP_HEADLESS	Run browser headless
CRAWL4AI_MCP_PROXY	Upstream proxy, supports http://, https://, socks5://, host:port, and port-only
CRAWL4AI_MCP_COOKIES_JSON	Playwright storage state JSON
CRAWL4AI_MCP_YTDLP_COOKIES_FROM_BROWSER	Browser cookies source for video transcript extraction, e.g. chrome, firefox:default
CRAWL4AI_MCP_YTDLP_COOKIEFILE	Netscape cookies.txt path for yt-dlp video transcript extraction
CRAWL4AI_MCP_USE_PERSISTENT_CONTEXT	Reuse browser profile
CRAWL4AI_MCP_USER_DATA_DIR	Profile directory
CRAWL4AI_MCP_NAVIGATION_TIMEOUT_MS	Default max single navigation wait, default 30000
CRAWL4AI_MCP_WAIT_UNTIL	Default page readiness strategy, default load
OPENAI_BASE_URL	OpenAI-compatible base URL
OPENAI_API_KEY	API key
OPENAI_MODEL	Model name

---

One-shot CLI

This project now exposes a stateless one-shot CLI in addition to the MCP stdio server.

Fetch a single URL once and print JSON:

crawl4agent fetch "https://obsidian.md/help/cli" --format markdown

Search the web once and print JSON:

crawl4agent search "agent framework" --engine auto --max-results 5

Use proxy and browser cookies for video transcript extraction:

crawl4agent fetch "https://www.youtube.com/watch?v=OFfwN23hR8U" \
  --proxy http://127.0.0.1:7890 \
  --cookies-from-browser chrome

Run golden smoke once and print a JSON array:

crawl4agent smoke --out-dir ./_golden_outputs

The existing crawl4ai-mcp command remains the MCP stdio server entrypoint for MCP hosts.

Available help surfaces:

crawl4agent --help
crawl4agent fetch --help
crawl4agent search --help
crawl4agent smoke --help

---

Golden smoke regression

CRAWL4AI_MCP_SMOKE_DIR=./_golden_outputs .venv/bin/python -m crawl4ai_mcp.smoke_golden

For overseas video URLs, a local proxy is often needed:

CRAWL4AI_MCP_PROXY=http://127.0.0.1:7890 \
CRAWL4AI_MCP_SMOKE_DIR=./_golden_outputs \
.venv/bin/python -m crawl4ai_mcp.smoke_golden

This writes full markdown outputs to _golden_outputs/ so you can inspect extraction quality page by page.

The golden set now includes the earlier baseline URLs plus ainew.me, openclaw, watcha, producthunt, mydrivers, caihongtu, openrouter, mobile Douban, and video pages from YouTube / Bilibili. For sites outside mainland China, proxy-based verification is recommended.

Some overseas sites may still return Cloudflare or similar verification pages even when a proxy is configured. In those cases the server now marks them with blocked=true. The recommended path is: better proxy quality, valid cookies, or a persistent browser profile after manual verification.

For some video golden URLs, subtitle extraction may require login. If yt-dlp reports login-required subtitles, configure either CRAWL4AI_MCP_YTDLP_COOKIES_FROM_BROWSER or CRAWL4AI_MCP_YTDLP_COOKIEFILE before running golden smoke.

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Prior art

• Crawl4AI: https://github.com/unclecode/crawl4ai
• mcp-crawl4ai-rag: https://github.com/coleam00/mcp-crawl4ai-rag
• weidwonder/crawl4ai-mcp-server: https://github.com/weidwonder/crawl4ai-mcp-server
• WaterCrawl: https://github.com/watercrawl/WaterCrawl
• teracrawl: https://github.com/BrowserCash/teracrawl

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License

This project is licensed under AGPL-3.0-or-later.