snippy — security grade SAFE, quality 70/100

Security audit verdict: SAFE · quality 70/100

No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

by Azure-Samples · MCP Server · ★ 114

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

🔒 Is snippy safe to install? View the security audit →

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

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

Quick Facts

Stars114
Forks1,222
LanguagePython
CategoryMCP Server
LicenseMIT
Quality Score70.268886078128/100
Open Issues7
Last Updated2025-12-18
Created2025-04-24
Platformsmcp, python
Est. Tokens~577k

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

Looking for a snippy alternative? If you're comparing snippy with other mcp server tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.

  • roam-code by Cranot · ⭐ 518

    Local codebase intelligence CLI + MCP server for AI coding agents: SQLite code graph, 28 languages, 287 comman

  • web-agent-protocol by OTA-Tech-AI · ⭐ 509

    🌐Web Agent Protocol (WAP) - Record and replay user interactions in the browser with MCP support

  • mcpstore by whillhill · ⭐ 427

    开盒即用的优雅管理mcp服务 | 结合Agent框架 | 作者听劝 | 已发布pypi | Vue页面demo

  • postman-mcp-server by postmanlabs · ⭐ 316

    Postman MCP Server — connect AI agents (Claude Code, Cursor, VS Code Copilot, Gemini CLI) to your Postman coll

  • toolhive-studio by stacklok · ⭐ 167

    ToolHive is an application that allows you to install, manage and run MCP servers and connect them to AI agent

  • mcp by taskade · ⭐ 164

    🤖 Taskade MCP · Official MCP server and OpenAPI to MCP codegen. Build AI agent tools from any OpenAPI API and

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Frequently Asked Questions

What is snippy?

snippy is 🧩 Build AI-powered MCP Tools with Azure Functions, Durable Agents & Cosmos vector search. Features orchestrated multi-agent workflows using OpenAI.. It is categorized as a MCP Server with 114 GitHub stars.

What programming language is snippy written in?

snippy is primarily written in Python. It covers topics such as ai-agents, azure-functions, copilot.

How do I install or use snippy?

You can find installation instructions and usage details in the snippy GitHub repository at github.com/Azure-Samples/snippy. The project has 114 stars and 1222 forks, indicating an active community.

What license does snippy use?

snippy is released under the MIT license, making it free to use and modify according to the license terms.

What are the best alternatives to snippy?

The top alternatives to snippy on Agent Skills Hub include roam-code, web-agent-protocol, mcpstore. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.

How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. 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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