amazon-lyr

amazon-lyr

Enables AI agents to research and shop on Amazon using their own logged-in browser session, supporting product search, details, reviews, cart, wishlist, and order tracking without API keys.

Category
Visit Server

README

<p align="center"> <img src="https://github.com/ishan-parihar/amazon-lyr/actions/workflows/ci.yml/badge.svg" alt="CI"> <img src="https://img.shields.io/badge/python-3.11%2B-2b6cb0?style=flat&logo=python&logoColor=white" alt="Python 3.11+"> <img src="https://img.shields.io/badge/tools-21-c026d3?style=flat&logo=modelcontextprotocol&logoColor=white" alt="21 MCP tools"> <img src="https://img.shields.io/badge/license-MIT-059669?style=flat" alt="MIT"> </p>

<h1 align="center">amazon-lyr</h1>

<p align="center"> <strong>The full Amazon lifecycle as MCP tools for AI agents</strong> — research products and reviews, manage the cart, place orders, track shipments, start returns — all through your own logged-in browser session. No PA-API keys. </p>

<p align="center"> <img src="./assets/readme/hero.svg" width="100%" alt="amazon-lyr: an agent session panel showing search_products, review_insights and checkout_preview flowing into a highlighted place_order step and a green order-confirmation chip"> </p>


An agent session, start to finish

This is one real conversation with an agent running amazon-lyr — no steps hidden:

1. search_products("noise cancelling headphones", max_price=300, min_rating=4)
2. review_insights("B0X1234567")            # star spread + keyword themes
3. search_in_reviews("B0X1234567", query="broke")     # what actually breaks?
4. add_to_cart("B0X1234567", quantity=1)
5. checkout_preview()                        # totals + address + payment, nothing bought
6. place_order(confirm="PLACE_ORDER")       # only after the user said "buy it"
   ✓ order placed · #111-2223334-5556667
7. track_shipment("111-2223334-5556667")
8. initiate_return("111-2223334-5556667", reason="defective", asin_hint="…")
9. list_returns()                           # refund status confirmed

Every step is a typed MCP tool call. The buying step is deliberately hard to fire by accident — see Safety model.

Why it's different

No API keys Speaks Amazon's own web endpoints over curl_cffi with Chrome TLS impersonation — the same session your browser has
Full lifecycle Not just scraping: cart mutations, wishlist writes, checkout with confirmation parsing, returns wizard, cancellations
Agent-native errors CAPTCHAs, sign-out redirects and soft throttles surface as typed errors with recovery hints (possibly_throttled, manual-fallback URLs) — never silent garbage

How it works

browser cookies ──▶ AmazonClient (curl_cffi, captcha-aware)
                        │
                        ▼
              bs4 parsers ──▶ normalized dicts ──▶ 21 FastMCP tools
                                                   (readOnly / destructive annotations)

Auth is your own session: amazon-lyr login imports cookies from Chrome, Brave, Firefox, Edge or Zen via obscura-core, or from an explicit JSON export. Reads need the login token; cart, orders, returns and checkout also need the session context cookies.

Install

# Persistent install — uv tool (isolated; bootstraps uv on clean systems)
curl -sSL https://raw.githubusercontent.com/ishan-parihar/amazon-lyr/main/install.sh | bash

# Zero-install run without persistence:
uvx --from git+https://github.com/ishan-parihar/amazon-lyr amazon-lyr --version

Then authenticate once and serve:

amazon-lyr login --browser chrome            # or: --cookies-file cookies.json
amazon-lyr search "mechanical keyboard"      # shell works immediately
amazon-lyr serve                             # MCP server (stdio)
{
  "mcpServers": {
    "amazon": {
      "command": "amazon-lyr",
      "args": ["serve"]
    }
  }
}

Tools (21)

Category Tools
Search search_products (department, price range, min-rating filters)
Product get_product, compare_products (up to 5 ASINs)
Reviews get_reviews, review_insights, search_in_reviews
Cart add_to_cart, view_cart, update_cart_quantity, remove_from_cart
Wishlist add_to_wishlist, get_wishlist
Orders get_orders, get_order_details, track_shipment
Returns list_returns, initiate_return (friendly reason codes)
Buying checkout_preview, place_order
Danger zone cancel_order
Session get_session_status

Safety model

Money moves through exactly two gates:

  1. Session gate — every mutation requires full login cookies (at-main + session-id/ubid-main). Reads-only sessions are refused before any request is made.
  2. Consent gate — place_order refuses to run unless passed the exact literal confirm="PLACE_ORDER". The intended flow is: preview for the user → user approves → then pass the literal. cancel_order and initiate_return are similarly annotated as destructive/non-idempotent so agent frameworks treat them accordingly.

Card verification (OTP / 3-D Secure) is detected mid-checkout and reported as needs_verification with browser instructions — never guessed through.

Limits

Honest ceilings, all surfaced as typed errors instead of wrong data:

  • CAPTCHA walls — account-area pages are stricter than the catalog; re-import fresh cookies or wait it out (CaptchaError includes hints)
  • Soft throttles — sub-3-second cadences get silent empty grids; responses carry possibly_throttled: true when suspected
  • Geo price variants — some variants render prices client-side; search falls back to text-scanning, get_product stays authoritative
  • No auto-buy without consent — by design; checkout demands the explicit confirm literal and user approval

Development

git clone https://github.com/ishan-parihar/amazon-lyr && cd amazon-lyr
uv sync --extra dev
uv run ruff check . && uv run pytest -q

Architecture notes, invariants, and the release process live in AGENTS.md.

License

MIT © Ishan Parihar

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured