Guides
Playwright proxy configuration/guides/playwright-proxyFixing 403 errors when scraping/guides/scraping-403Handling 429 rate limits/guides/scraping-429Python rotating proxies/guides/python-rotating-proxyMCP web scraping setup/guides/mcp-web-scraping
Use cases
Best API for Playwright/use-cases/playwrightWeb access for AI agents/use-cases/ai-agent
Providers
Bright Data/providers/bright-dataZenRows/providers/zenrowsOxylabs/providers/oxylabsApify/providers/apifyDecodo/providers/decodoScraperAPI/providers/scraperapi
Comparisons
Bright Data vs Oxylabs/compare/bright-data-vs-oxylabsZenRows vs ScraperAPI/compare/zenrows-vs-scraperapiBright Data + Playwright vs ZenRows + Playwright/compare/bright-data-vs-zenrows?workload=playwright
Benchmarks
Browser API benchmark · 30D/benchmarks/browser-apiScraping API benchmark · 30D/benchmarks/scraping-api
AI agents

MCP web scraping setup

Define a narrow web-fetch tool boundary with explicit limits and provenance.

FetchGauge technical notes · examples run only when you execute them

Define the tool boundary

Decide which documents a tool is allowed to retrieve and how its caller will receive source URLs, timestamps and errors. Keep retrieved content separate from trusted instructions.

Set explicit budgets

Set request, response-size and concurrency budgets. Treat a tool result as untrusted source content and preserve provenance when passing it to an agent.

Evaluate before connecting

Use fixed fixtures to exercise success, timeout and invalid-content cases. The local recommendation engine ranks only the sample capabilities in the canonical dataset; no LLM or MCP server runs in this deployment.

Compare with context

FetchGauge’s current benchmark data is simulated. Use the methodology to understand the intended protocol, and the calculator to explore its cost assumptions.