snippy: Build AI-powered MCP Tools with Azure Functions, Durable Agents & Cosmos vector search

by Azure-Samples · MCP Server · ★ 114

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

Security audit verdict: SAFE · quality 70/100

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.

ai-agentsazure-functionscopilotcosmosdbdurable-functionsgithub-copilotmcpmcp-servermcp-toolsmulti-agent

Quick Facts

Stars114
Forks1,222
LanguagePython
CategoryMCP Server
LicenseMIT
Quality Score70/100
Open Issues7
Last Updated2025-12-18
Created2025-04-24
Platformsmcp, python
Est. Tokens~577k

About snippy

Snippy is an Azure Functions-based reference application that demonstrates how to build MCP (Model Context Protocol) tools that integrate with AI assistants like GitHub Copilot. It showcases a modern serverless AI application architecture where Azure Functions serve as both traditional APIs and MCP-compatible tools that AI assistants can discover and use. ![Snippy Architecture](i

Compatible Skills

These tools work well together with snippy for enhanced workflows:

  • CodeRAG — semantic(0.25)+complementary+shared_fw(openai)+same_lang+similar_pop+shared_platform (62%)
  • relevanceai — semantic(0.27)+complementary+rare_topics+same_lang+similar_pop+shared_platform (59%)
  • openai-agents-python — semantic(0.15)+complementary+shared_fw(openai)+same_lang+shared_platform (53%)
  • azure-agentic-infraops — semantic(0.19)+complementary+same_lang+similar_pop+shared_platform (52%)

snippy alternative? Top 6 similar tools

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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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