What is amazon_ads_mcp AGENTS.md?

A set of project instructions for developing Amazon Ads MCP, a Python server that connects AI assistants to Amazon's advertising services through the Model Context Protocol (MCP).

Use it when installing, running, checking, or contributing to the Amazon Ads MCP project. It covers Python and uv setup, Docker startup, Claude connection, linting, and tests.

It gives coding agents and developers a required setup and validation process, reducing missed dependencies, linting problems, and failed tests.

How KuudoAI/amazon_ads_mcp configures Codex and OpenCode

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Borrowing it

Nothing to install: this file belongs to KuudoAI/amazon_ads_mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file

curl -O https://raw.githubusercontent.com/KuudoAI/amazon_ads_mcp/main/AGENTS.md

Clone the repo

git clone --depth 1 https://github.com/KuudoAI/amazon_ads_mcp

Made for: Codex, OpenCode.

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README.md

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Per session 7,786 This file is loaded in full into every session.

When invoked 7,786 The same file — it is already loaded in full.

Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.07786	$0.07786
Opus 5.5	$0.03114	$0.03114
Sonnet 5.5	$0.01557	$0.01557
Haiku 4.5	$0.00779	$0.00779

Measured 2mo ago against content hash 24b1203dc6b7, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

amazon_ads_mcp AGENTS.md scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

4. **Download via HTTP**: Open URL in browser or use curl

AGENTS.md · 871 lines

How it starts

The opening of the file, as written. The whole thing — 871 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Amazon Ads MCP Development Guidelines

Audience: LLM-driven engineering agents and human developers

Amazon Ads MCP is a Python framework (Python ≥3.10) for integrating Amazon Advertising API with Model Context Protocol (MCP) servers. This project provides a complete toolkit for building AI-powered advertising applications with comprehensive campaign management, reporting, and optimization capabilities.

Do This First (for Agents)

Ensure Python ≥3.10 and uv are installed
uv sync to install dependencies
Start the server: docker compose up -d
Connect Claude to the MCP server (HTTP):
- claude mcp add amazon-ads-mcp -- python -m amazon_ads_mcp.server --transport http --port 9080
Verify: claude mcp list and use /mcp inside Claude

Required Development Workflow

CRITICAL: Always run these commands in sequence before committing:

# Install dependencies
uv sync                              # Install dependencies

# Validate code
uv run ruff check --fix             # Lint and auto-fix
uv run pytest                        # Run full test suite

All must pass - tests/linting must be clean before committing.

Agent Ops (LLM Guidance)

Preambles: Send a brief 1–2 sentence note before running tool commands.
Plans: Use TodoWrite for multi-step work; keep exactly one in_progress step.
Edits: Use Edit or MultiEdit to modify files; keep changes focused and avoid unrelated edits.
Testing: Run the smallest relevant tests first; do not fix unrelated failures.
Sandboxing: Assume workspace-write FS and restricted network; prefer local resources over external APIs unless keys are present.

Agent Success Playbook

Follow these steps for reliable outcomes in Claude contexts:

Understand & Plan

Clarify task type: API integration, MCP connectivity, Docker, tests, or GitHub workflow.
Post a short preamble and, for multi-step work, create a minimal TodoWrite with exactly one in_progress step.

Connect & Verify (MCP + Server)

Start server: docker compose up -d (Amazon Ads MCP at http://localhost:9080).
Add MCP to Claude (HTTP):
- claude mcp add amazon-ads-mcp -- python -m amazon_ads_mcp.server --transport http --port 9080
Verify in Claude: claude mcp list then /mcp → run a tool (e.g., list profiles).

Read the full file on GitHub · 871 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

2mo ago First seen · 871 lines · 7,786 tokens per session scan A 24b1203dc6b7

Subscribe to this mod's changes

amazon_ads_mcp AGENTS.md is an instructions file published in the GitHub repository KuudoAI/amazon_ads_mcp (73 stars, last pushed yesterday), licensed MIT. It adds 7,786 tokens to every session, about $0.0311 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Marketing and SEO amazon amazon-ads amazon-ads-api amazon-ads-mcp amazon-advertising amazon-advertising-api

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amazon_ads_mcp: Instructions file for Codex