quickstart-resources: A repository of servers and clients from the Model Context Protocol tutorials

by modelcontextprotocol · MCP Server · ★ 1.2k

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Security audit verdict: SAFE · quality 47/100

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.

Quick Facts

Stars1,234
Forks701
LanguageTypeScript
CategoryMCP Server
Quality Score47/100
Open Issues12
Last Updated2026-11-06
Created2024-12-11
Platformsnode
Est. Tokens~14k

About quickstart-resources

Quickstart Resources A repository of servers and clients from the following Model Context Protocol tutorials: Quickstart – a simple MCP weather server Building MCP clients – an LLM-powered chatbot MCP client Security Note These examples are intentionally minimal.

Compatible Skills

These tools work well together with quickstart-resources for enhanced workflows:

  • kubernetes-mcp-server — semantic(0.16)+complementary+same_lang+similar_pop+shared_platform (56%)
  • mcp-golang — semantic(0.18)+complementary+same_lang+similar_pop+shared_platform (51%)
  • aks-mcp — semantic(0.18)+complementary+same_lang+similar_pop+shared_platform (51%)
  • mcp-language-server — semantic(0.18)+complementary+same_lang+similar_pop+shared_platform (51%)
  • mcp-filesystem-server — semantic(0.16)+complementary+same_lang+similar_pop+shared_platform (51%)

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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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