fetcher-mcp: MCP server for fetch web page content using Playwright headless browser

by jae-jae · MCP Server · ★ 1.1k

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

How to install fetcher-mcp

npx -y fetcher-mcp
npx -y fetcher-mcp --log --transport=http --host=0.0.0.0 --port=3000

Taken from the project's README on GitHub. Check the security audit before running it.

Security audit verdict: SAFE · quality 81/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

aimcpplaywright

Quick Facts

Stars1,088
Forks102
LanguageTypeScript
CategoryMCP Server
LicenseMIT
Quality Score81/100
Open Issues7
Last Updated2026-11-05
Created2025-03-19
Platformsbrowser, mcp, node
Est. Tokens~12k

About fetcher-mcp

中文 | Deutsch | Español | français | 日本語 | 한국어 | Português | Русский Fetcher MCP MCP server for fetch web page content using Playwright headless browser. 🌟 Recommended: OllaMan - Powerful Ollama AI Model Manager.

Compatible Skills

These tools work well together with fetcher-mcp for enhanced workflows:

  • playwright-mcp — semantic(0.51)+shared_fw(playwright)+rare_topics+same_lang+similar_pop+shared_platform (60%)
  • playwright-mcp — semantic(0.65)+shared_fw(playwright)+rare_topics+same_lang+shared_platform (60%)
  • dev-browser — semantic(0.22)+complementary+rare_topics+same_lang+similar_pop+shared_platform (57%)
  • intelli-browser — semantic(0.21)+complementary+shared_fw(playwright)+rare_topics+same_lang+shared_platform (55%)
  • playwright-skill — semantic(0.18)+complementary+shared_fw(playwright)+rare_topics+similar_pop+shared_platform (54%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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