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AiAgentKarl

Agentic Product Protocol MCP Server

Agentic Product Protocol MCP 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

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