by danilop · MCP Server · ★ 109
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
npx -y @smithery/cli install @danilop/MCP2Lambda --client claudeTaken from the project's README on GitHub.
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.
| Stars | 109 |
| Forks | 21 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 77/100 |
| Open Issues | 5 |
| Last Updated | 2026-10-27 |
| Created | 2025-02-25 |
| Platforms | aws, mcp, python |
| Est. Tokens | ~10k |
MCP2Lambda Run any AWS Lambda function as a Large Language Model (LLM) tool without code changes using Anthropic's Model Context Protocol (MCP). This MCP server acts as a bridge between MCP clients and AWS Lambda functions, allowing generative AI models to access and run Lambda functions as tools.
Looking for a MCP2Lambda alternative? If you're comparing MCP2Lambda 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.
A minimal yet powerful framework for creating AI agents with full control over tools, providers, and execution
An introduction to the world of AI Agents
Talk with your AWS using Claude. Model Context Protocol (MCP) server for AWS. Better Amazon Q alternative.
Creates a simple MCP tool server with "streaming" HTTP.
MCP server + embedded terminal that lets Claude Code see and edit your ComfyUI workflows
Serverless AI agent using LangChain.js and Model Context Protocol (MCP) integration to order burgers from a bu
Explore other popular mcp server tools:
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: