amazon_ads_mcp: Model Context Protocol server for Amazon Advertising API

by KuudoAI · MCP Server · ★ 73

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

How to install amazon_ads_mcp

git clone https://github.com/KuudoAI/amazon-ads-mcp.git
docker run -d --env-file .env -e CODE_MODE=false -e PORT=9080 -p 9080:9080 amazon-ads-mcp:latest

Taken from the project's README on GitHub.

Security audit verdict: SAFE · quality 72/100

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-agentamazonamazon-adsamazon-ads-apiamazon-ads-mcpamazon-advertisingamazon-advertising-apichatgptclaudeclaude-code

Quick Facts

Stars73
Forks24
LanguagePython
CategoryMCP Server
LicenseMIT
Quality Score72/100
Open Issues5
Last Updated2026-10-14
Created2025-09-19
Platformsclaude-code, mcp, python
Est. Tokens~24k

About amazon_ads_mcp

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)...

Compatible Skills

These tools work well together with amazon_ads_mcp for enhanced workflows:

  • serverless-rag-demo — semantic(0.32)+complementary+same_lang+similar_pop+shared_platform (56%)
  • review-analyzer-skill — semantic(0.16)+complementary+rare_topics+same_lang+similar_pop+shared_platform (55%)

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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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