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 danilop · MCP Server · ★ 110
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is MCP2Lambda safe to install? View the security audit →
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. This is useful, for example, to access private resources such as internal applications and databases without the need to provide public network access. This approach allows the model to use other AWS services, private networks, a
| Stars | 110 |
| Forks | 20 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 77.365111238584/100 |
| Open Issues | 3 |
| Last Updated | 2025-03-27 |
| Created | 2025-02-25 |
| Platforms | aws, mcp, python |
| Est. Tokens | ~7k |
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MCP2Lambda is Run any AWS Lambda function as a Large Language Model (LLM) tool without code changes using Anthropic's Model Context Protocol (MCP).. It is categorized as a MCP Server with 110 GitHub stars.
MCP2Lambda is primarily written in Python. It covers topics such as ai, aws, aws-lambda.
You can find installation instructions and usage details in the MCP2Lambda GitHub repository at github.com/danilop/MCP2Lambda. The project has 110 stars and 20 forks, indicating an active community.
MCP2Lambda is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to MCP2Lambda on Agent Skills Hub include zypher-agent, oreilly-ai-agents, aws-mcp. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
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: