PyScrappy

PyScrappy: Python web scraping toolkit + MCP server for AI agents

PyScrappy is an AI-native web scraping toolkit that turns websites into structured, LLM-ready data. Use it as a Python library or expose it as an MCP server for AI agents.

📖 Documentation: pyscrappy.vercel.app

Key features

• Generic scraper — give it any URL, get back structured text, links, images, tables, and metadata
• LLM-ready output — .to_markdown() turns any result into clean Markdown; also .to_json() and .to_dataframe()
• MCP server — expose the scrapers as tools for AI agents (Claude, Cursor, local LLMs, …)
• JS rendering — optional Playwright backend for JavaScript-heavy sites
• Custom selectors — pass CSS selectors to extract exactly what you need
• Concurrent scraping — scrape_many / scrape_all run scrapes in parallel
• Proxy & scraping-API support — route through a proxy or ScraperAPI/ScrapeOps for blocked sites
• Retry & rate-limiting — built-in exponential backoff and per-domain rate limiting
• Type-safe — full type hints, py.typed marker
• 20+ built-in scrapers — Wikipedia, IMDB, stocks, news, GitHub, Amazon/IKEA, YouTube, and more

Installation

pip install pyscrappy

Optional extras:

# Browser support (for JS-rendered pages)
pip install 'pyscrappy[browser]'
playwright install chromium

# DataFrame support
pip install 'pyscrappy[dataframe]'

# MCP server (use PyScrappy's scrapers as AI-agent tools)
pip install 'pyscrappy[mcp]'

# Everything
pip install 'pyscrappy[all]'

For AI agents

PyScrappy ships an MCP server that exposes its scrapers as tools, so an agent (Claude, Cursor, an OpenAI agent, a local LLM) can pull structured web data from any URL and hand it straight to the model:

AI agent  ──MCP tool call──▶  PyScrappy  ──fetch + extract──▶  Any website
   ▲                                                                │
   └──────────────  clean Markdown / JSON  ◀───────────────────────┘

pip install 'pyscrappy[mcp]'
claude mcp add pyscrappy pyscrappy-mcp

Then just ask: "use pyscrappy to summarize the latest headlines from bbc.com." See MCP server for the full setup and tool list.

Local models (Ollama), no MCP host needed

Ollama can't talk MCP on its own, so normally you'd run a host (Goose, Cline, …) in between. PyScrappy skips that with a built-in agent that talks to Ollama directly and lets a local model call the scrapers as tools:

pip install 'pyscrappy[mcp]'                 # needs Python 3.10+
pyscrappy chat --model qwen2.5 "what's the current AAPL quote?"

It exposes the same 22 tools as the MCP server. The only requirement is a model that supports tool calling (Llama 3.1, Qwen 2.5, Mistral, …); how well it picks the right tool is up to the model. Point it at a remote Ollama with --host, and pass -v to see each tool call.

MCP server (use PyScrappy from an AI agent)

PyScrappy ships an optional Model Context Protocol server, so an AI agent (e.g. Claude) can call PyScrappy's scrapers as tools and get structured web data back.

pip install 'pyscrappy[mcp]'

The MCP extra installs the standalone fastmcp package and requires Python 3.10 or newer. On Python 3.9 the core scraping library still works, but the MCP server is unavailable.

This installs the pyscrappy-mcp command. It uses stdio by default for local MCP clients; Streamable HTTP and legacy SSE are available for remote deployments:

pyscrappy-mcp          # stdio (default)
pyscrappy-mcp --http   # Streamable HTTP
pyscrappy-mcp --sse    # legacy SSE

You can also run the stdio server with python -m pyscrappy.mcp.

Register with Claude Code

claude mcp add pyscrappy pyscrappy-mcp

Register with Claude Desktop

Add to your claude_desktop_config.json and restart the app:

{
  "mcpServers": {
    "pyscrappy": {
      "command": "pyscrappy-mcp"
    }
  }
}

Tip: Claude Desktop does not inherit your shell PATH. If pyscrappy-mcp is not found, use the absolute path to the command (e.g. the one printed by which pyscrappy-mcp).

Available tools

The lookup_movie tool needs a free OMDb API key. Pass it to the server through your MCP client config, e.g. for Claude Desktop:

{
  "mcpServers": {
    "pyscrappy": {
      "command": "pyscrappy-mcp",
      "env": { "OMDB_API_KEY": "your-key" }
    }
  }
}

Once registered, just ask the agent naturally, e.g. "use pyscrappy to get the latest headlines from bbc.co.uk and the AAPL stock quote."

Built-in scrapers

Every scraper that works without a proxy is also exposed as an MCP tool (last column).

Plugins

PyScrappy is extensible: you can add your own scrapers, and third parties can ship them as standalone pyscrappy-<name> packages. A registered scraper works everywhere a built-in does, including the MCP server and the pyscrappy chat agent, with no change to PyScrappy core.

In your own code — register with the decorator:

from pyscrappy import BaseScraper, register_scraper, get_scraper
from pyscrappy.core.models import ScrapeResult, ScrapeMetadata

@register_scraper("reddit")
class RedditScraper(BaseScraper):
    def scrape(self, subreddit: str, **kwargs) -> ScrapeResult:
        data = self.fetch_and_parse(f"https://old.reddit.com/r/{subreddit}/.json")
        # ... build a list of dicts ...
        return ScrapeResult(data=[...], metadata=ScrapeMetadata(scraper="reddit"))

get_scraper("reddit")().scrape(subreddit="python")

As a distributable package — advertise an entry point in your pyproject.toml, and PyScrappy discovers it once your package is installed:

[project.entry-points."pyscrappy.scrapers"]
reddit = "pyscrappy_reddit:RedditScraper"

After pip install pyscrappy-reddit, the scraper shows up in list_scrapers(), and an AI agent can call it via the scrape_with MCP tool — no core change required.

First-class MCP tools (optional). Add an mcp_tools mapping and your scraper becomes a dedicated, typed MCP tool instead of only being reachable through the generic scrape_with — its schema is derived from the method signature, so agents get proper named arguments:

@register_scraper("reddit")
class RedditScraper(BaseScraper):
    mcp_tools = {"search_reddit": "scrape"}   # tool name -> method

    def scrape(self, subreddit: str, sort: str = "hot") -> ScrapeResult:
        ...

See the plugin template for a complete, copyable starting point, and the plugin guide for the full walkthrough.

Quick start

Scrape any URL → clean, LLM-ready Markdown

from pyscrappy import scrape

result = scrape("https://en.wikipedia.org/wiki/Web_scraping")

print(result.to_markdown())   # feed straight to an LLM
# ...or result.to_json() / result.to_dataframe()

Prefer raw fields? Every result is a ScrapeResult with .data (a list of dicts):

print(result.data[0]["metadata"]["title"])
print(result.data[0]["text"]["word_count"])

Custom CSS selectors

from pyscrappy import GenericScraper

with GenericScraper() as gs:
    result = gs.scrape(
        url="https://news.ycombinator.com",
        selectors={"title": ".titleline a", "score": ".score"},
    )
    for item in result.data:
        print(item["title"], item.get("score", ""))

Site-specific scrapers

Every built-in scraper follows the same pattern — instantiate, scrape(...), read result.data (or .to_dataframe() / .to_markdown()):

from pyscrappy import WikipediaScraper

with WikipediaScraper() as ws:
    result = ws.scrape(query="Python (programming language)", mode="summary")
    print(result.data[0]["text"])

Each scraper has its own arguments (Wikipedia, stocks, IMDB, news, YouTube, Amazon/Newegg/IKEA, Uber Eats, and more — see the full list). For per-scraper arguments and examples, see the documentation.

Configuration

from pyscrappy import ScraperConfig, GenericScraper

config = ScraperConfig(
    timeout=20.0,            # request timeout in seconds
    max_retries=3,           # retry failed requests
    rate_limit=2.0,          # seconds between requests per domain
    proxy="http://...",      # proxy URL, or a list to rotate through
    scraper_api=None,        # route via a scraping-API service (see below)
    headless=True,           # browser runs headless
    render_js="auto",        # auto-detect if JS rendering is needed
    cache_ttl=0,             # response cache TTL in seconds (0 = disabled)
)

with GenericScraper(config) as gs:
    result = gs.scrape(url="https://example.com")

Proxies and blocked sites

Some sites (e.g. eBay, Instagram, Twitter/X, Spotify) block direct automated requests. PyScrappy supports two ways to get through them.

A proxy (or a rotating list) — applies to both the HTTP and browser backends:

from pyscrappy import ScraperConfig, AmazonScraper

# Single proxy
config = ScraperConfig(proxy="http://user:pass@host:port")

# Rotating list (one picked per request)
config = ScraperConfig(proxy=["http://p1:8080", "http://p2:8080"])

A scraping-API service (ScraperAPI, ScrapeOps, ScrapingBee) — routes requests through the service, which handles proxies and anti-bot challenges for you:

config = ScraperConfig(scraper_api={
    "provider": "scraperapi",   # or "scrapeops", "scrapingbee"
    "api_key": "YOUR_KEY",
    "render_js": True,           # optional
})

# Now any scraper works through the service, unchanged:
with AmazonScraper(config) as scraper:
    result = scraper.scrape(query="laptop")

This is the reliable way to use the scrapers marked "needs proxy" above.

Concurrent scraping

Scraping is I/O-bound, so running several scrapes at once parallelizes the network waits. scrape_many runs one scraper over many inputs; scrape_all runs a mix of scrapers together. Both preserve input order.

from pyscrappy import scrape_many, scrape_all, AmazonScraper, WikipediaScraper, NewsScraper

# One scraper, many queries, concurrently:
results = scrape_many(AmazonScraper, [{"query": "laptop"}, {"query": "phone"}])

# Different scrapers at once:
results = scrape_all([
    lambda: WikipediaScraper().scrape(query="Python"),
    lambda: NewsScraper().scrape(feed_url="https://rss.nytimes.com/services/xml/rss/nyt/World.xml"),
])

Response caching

Set cache_ttl to a positive number of seconds to cache successful GET responses. Repeated requests for the same URL (and query params) within the TTL are served from cache, skipping both the network and the rate limiter. Caching is disabled by default (cache_ttl=0).

from pyscrappy import WikipediaScraper
from pyscrappy import ScraperConfig

config = ScraperConfig(cache_ttl=300)   # cache for 5 minutes

with WikipediaScraper(config) as ws:
    ws.scrape(query="Python")   # fetched over the network
    ws.scrape(query="Python")   # served from cache

The cache is in memory and shared across scraper instances in the same process (so it also speeds up repeated calls through the MCP server), and is cleared when the process exits. Call HttpClient.clear_cache() to empty it manually.

Dependencies

Required: httpx, beautifulsoup4, lxml

Optional: playwright (JS rendering), pandas (DataFrames), fastmcp (MCP server, Python 3.10+)

License

MIT

Contributing

All contributions welcome. See Issues.

This package is for educational and research purposes.