mcp-server-rag-web-browser: A MCP Server for the RAG Web Browser Actor

by apify · MCP Server · ★ 206

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

How to install mcp-server-rag-web-browser

git clone https://github.com/apify/mcp-server-rag-web-browser.git

Taken from the project's README on GitHub.

Security audit verdict: SAFE · quality 68/100

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.

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.

Quick Facts

Stars206
Forks30
LanguageJavaScript
CategoryMCP Server
LicenseApache-2.0
Quality Score68/100
Last Updated2026-05-19
Created2024-12-06
Platformsbrowser, mcp, node
Est. Tokens~11k

About mcp-server-rag-web-browser

MCP Server for the RAG Web Browser Actor 🌐 Implementation of an MCP server for the RAG Web Browser Actor. This Actor serves as a web browser for large language models (LLMs) and RAG pipelines, similar to a web search in ChatGPT.

Compatible Skills

These tools work well together with mcp-server-rag-web-browser for enhanced workflows:

  • gemini-browser-agent — semantic(0.22)+complementary+same_lang+similar_pop+shared_platform (53%)
  • AgentLLM — semantic(0.18)+complementary+same_lang+similar_pop+shared_platform (51%)
  • browserable — semantic(0.18)+complementary+same_lang+similar_pop+shared_platform (51%)
  • nocodb-mcp-server — semantic(0.45)+same_lang+similar_pop+shared_platform (51%)
  • ClaudeChrome — semantic(0.16)+complementary+same_lang+similar_pop+shared_platform (50%)

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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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