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 KuudoAI · MCP Server · ★ 67
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is amazon_ads_mcp safe to install? View the security audit →
Amazon Ads API MCP SDK Build AI-powered advertising applications with the Model Context Protocol (MCP) SDK for Amazon Advertising API Made with ❤️ + ☕ by Openbridge MCP registry id (for clients and catalogs that display a stable package name): Table of contents What are MCP tools? What is Amazon Ads API MCP SDK? Quick start Installation Configuration (auth, packages, profiles, regions) Downloading reports and exports Example MCP client (Claude Desktop) Context limits Code mode Tool audit Background tasks Troubleshooting Documentation map What Are MCP Tools? Think of MCP (Model Context Protocol) as a translator between an AI model and outside system
| Stars | 67 |
| Forks | 18 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 73.4222288014478/100 |
| Open Issues | 4 |
| Last Updated | 2026-09-06 |
| Created | 2025-09-19 |
| Platforms | mcp, python |
| Est. Tokens | ~24k |
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amazon_ads_mcp is Amazon Ads MCP - Model Context Protocol server for Amazon Advertising API. It is categorized as a MCP Server with 67 GitHub stars.
amazon_ads_mcp is primarily written in Python. It covers topics such as amazon, amazon-ads, amazon-ads-api.
You can find installation instructions and usage details in the amazon_ads_mcp GitHub repository at github.com/KuudoAI/amazon_ads_mcp. The project has 67 stars and 18 forks, indicating an active community.
amazon_ads_mcp is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to amazon_ads_mcp on Agent Skills Hub include html-anything, sealeap-amazon-skills, review-analyzer-skill. 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: