Agentic Product Protocol Server

Klarna-style product discovery for AI shopping agents.

Makes product catalogs machine-readable so AI agents can search, compare, and purchase products programmatically — no screen scraping, no landing pages.

The Problem

Today's e-commerce is built for humans: landing pages, image carousels, "Add to Cart" buttons. AI shopping agents can't efficiently navigate this. They need structured product data — not HTML.

Klarna introduced the Agentic Product Protocol (December 2025) to solve exactly this: a standardized way for merchants to expose their product catalogs to AI agents. Think of it as RSS feeds, but for shopping.

What This Server Does

This MCP server implements the core ideas of agentic product discovery:

• Structured search results — not web pages, but clean JSON with name, price, nutrition, ratings
• Product comparison — side-by-side structured comparison across multiple dimensions
• Feed conversion — take any product feed (JSON, CSV, Open Food Facts) and normalize it into an agent-friendly schema
• Schema generation — convert raw product data into the Agentic Product Protocol format
• Availability checking — real-time product status in a machine-readable format

Uses Open Food Facts as a demo data source — works with any product feed.

Installation

pip install agentic-product-protocol-mcp

Or with uvx (no install needed):

uvx agentic-product-protocol-mcp

Configuration

Claude Desktop

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "product-protocol": {
      "command": "uvx",
      "args": ["agentic-product-protocol-mcp"]
    }
  }
}

Claude Code (CLI)

claude mcp add product-protocol -- uvx agentic-product-protocol-mcp

Tools

Tool	Description
search_products	Search products with structured results (name, nutrition, labels, stores)
get_product_details	Get full product data by barcode/ID
compare_products	Side-by-side comparison of 2-5 products
convert_feed	Convert JSON/CSV/OFF feeds into normalized agent schema
generate_product_schema	Generate Agentic Product Protocol schema from raw data
check_availability	Check product availability and store information

Example Usage

Search for products:

"Search for organic chocolate bars"

Compare products:

"Compare these three chocolate bars: 3017620422003, 7622210449283, 7613034626844"

Convert a feed:

"Convert this Open Food Facts search into agent-friendly format: https://world.openfoodfacts.org/cgi/search.pl?search_terms=protein+bar&page_size=10"

Generate schema:

"Generate an agentic product schema for this product data: {name: 'Widget Pro', price: 29.99, category: 'Electronics'}"

Why Structured Feeds > Landing Pages

	Landing Pages	Structured Feeds
Parsing	Screen scraping, fragile	Clean JSON, reliable
Speed	Load page → parse DOM → extract	Single API call
Accuracy	Layout changes break everything	Schema-validated
Comparison	Manual extraction per site	Normalized across sources
Agent UX	Built for human eyes	Built for agent consumption

Data Source

This server uses Open Food Facts as its demo data source — a free, open, community-built database of food products from around the world. No API key required.

For production use, connect your own product feeds using the convert_feed tool with JSON or CSV format.

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License

MIT