Table of contents

• Where this fits in Kuudo
• 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
• Frequently asked
• Troubleshooting
• Documentation map

Where this fits in Kuudo

This repository is the tool layer of a broader Amazon agent platform. Kuudo gives Amazon agents tools, knowledge, workflows, and a place to work. This repository is the tools.

Read this repository as one layer of that platform rather than as the whole of it. The operations below are what an agent can call; Skills are what it runs.

What Are MCP Tools?

Think of MCP (Model Context Protocol) as a translator between an AI model and outside systems (like Amazon Ads). Each MCP tool is like a remote control button that tells the AI how to interact with Amazon Ads. Without MCP tools, the AI would have no idea how to “talk” to Amazon Ads.

With MCP tools:

• The AI knows the exact endpoints to call.
• The AI can request campaign reports, budgets, or targeting data safely.
• Everything is structured, so the AI doesn’t break things by making random guesses.

👉 In short: MCP tools = a safe, well-labeled toolkit that lets AI work with the Amazon Ads API.

🚀 What is Amazon Ads API MCP SDK?

The Amazon Ads API MCP SDK is an open-source implementation that provides a robust foundation for creating AI-powered advertising tools, chatbots, and automated services.

✨ Key Features

• 🔌 MCP Integration: Full Model Context Protocol compliance for AI application integration
• 🌍 Multi-Region Support: NA, EU, and FE region endpoints with automatic routing
• 📊 Comprehensive API Coverage: Campaigns, profiles, reporting, DSP, AMC workflows, and more
• 📝 Type Safety: Full Pydantic model support with comprehensive type hints
• 🧪 Production Ready: Includes testing, validation, and error handling

🎯 Use Cases

Claude Desktop Integration

• Campaign Management: Ask Claude to create, update, or analyze campaigns
• Performance Insights: Get AI-powered analysis of your advertising performance
• Budget Optimization: Let Claude suggest budget adjustments based on performance
• Creative Testing: Get recommendations for ad creative improvements
• Reporting: Generate custom reports and insights on demand

AI Applications

• Marketing Chatbots: Build conversational AI that can manage Amazon Ads campaigns
• Automated Reporting: AI-powered insights and performance analysis
• Smart Budget Management: Intelligent budget optimization using AI
• Creative Optimization: AI-driven ad creative testing and optimization

Enterprise Services

• Marketing Automation Platforms: Integrate Amazon Ads into existing marketing tools
• Agency Management Systems: Multi-client, multi-account advertising management
• E-commerce Integrations: Connect Amazon Ads with e-commerce platforms
• Analytics Dashboards: Real-time advertising performance monitoring

Developer Tools

• API Wrappers: Create custom SDKs for specific use cases
• Testing Frameworks: Automated testing for Amazon Ads integrations
• Development Tools: Local development and debugging utilities

Why this Ads MCP is different

Other Amazon Ads MCPs do happy path. They fall apart the moment an agent makes a reasonable mistake and Amazon's Ads API has more reasonable mistakes than almost any other large API surface. The v1 reporting catalog uses a different vocabulary than v3. The docs are scattered across migration generations. The same field can have three plausible names depending on which tutorial an agent or human was trained on.

You've felt this before

You know the symptoms even if you've never thought of them as a single problem:

• "Compacting conversation…" right when you were getting somewhere
• "Usage limit reached" mid-task, with nothing to show for the tokens
• "Context window exceeded" start over, re-explain everything
• The agent quietly forgetting the fix it figured out three turns ago
• Five tool calls, four of them retries, none of them progress
• Re-pasting the same error into the chat for the third time

That's not the model being dumb. That's the API surface burning context vague errors trigger thrashing, thrashing fills the window, the window fills and everything useful gets evicted. Anywhere from 4 to 12 round-trips for an agent: wrong field names, wrong body shape, wrong filter operator, wrong date placement, wrong advertiser-account format.

Here, the wrong moves return the right move

This server is built around a different premise: the error surface is the documentation surface. Every failure is a teaching opportunity, and the server is engineered to make the next attempt smarter than the last one, for the same agent or the next one, without retraining, prompt updates, or memory tricks.

• The server tells you what went wrong, and why you got it wrong
• Every failure converts to a corrective action — the model just reads the hint and moves on
• A five-tool-call debugging arc collapses into a two-call corrected one
• The same mistake a week later doesn't re-burn context, because the validator, alias table, and deprecated-shape table are still right there
• No out-of-context tribal knowledge to forget

Every failure is cheaper than the last. Every agent learns from the same authoritative source. Every retry has direction. That compounding is what a smart agent solution looks like in practice.

Everything is an edge case, you can help

We won't catch every edge case. Amazon's API surface is enormous, the migration history is messy, and real-world failures get more creative than anything anyone can anticipate. What we're committing to is the strategy that errors should teach, that documentation should live in the surfaces agents actually touch, that every wrong move should make the next move easier.

If you hit a failure where the envelope didn't help; vague hint, wrong suggestion, no hint at all, that's exactly the feedback we want. Open an issue, paste the envelope, tell us what you expected. The strategy is only as good as the cases it covers.

Quick start

Prerequisites: Docker (recommended), Python 3.10+ if you run from source, and Amazon Ads API access (your own developer app or a partner such as Openbridge).

1. git clone https://github.com/KuudoAI/amazon-ads-mcp.git && cd amazon-ads-mcp
2. cp .env.example .env and add credentials (see Configuration).
3. docker compose up -d
4. Connect your MCP client to http://localhost:<PORT>/mcp/ — .env.example sets PORT=9080 (override in .env if needed).

OAuth steps, client JSON samples, and a full variable reference: Configuration below, INSTALL.md, and AGENTS.md.

📚 What Is Included In the Amazon Ads MCP?

The MCP server mirrors broad coverage of the Amazon Ads API surface. Each enabled package maps to a set of API operations. That includes Campaign Management (Amazon Ads API v1), Exports, Amazon Marketing Cloud, and many more.

Here is a representative list of the various Amazon API services in the MCP: