Find AI-ready merchant feeds: resolve a store domain to its ID and fetch full product detail.
Quick Install
Automated & IDE SetupCopy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.
Manual Client & Custom JSON ConfigExpand JSON ▾
Client Config & Setup
Choose your client or environment
Target File:
💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.
Documentation Overview
Agenticfeed Standard
An open specification for making ecommerce product catalogues readable by AI shopping agents.
AI assistants like ChatGPT, Claude, Perplexity, and Gemini are already recommending products to millions of shoppers. They do not browse category pages or read banner ads. They consume structured data and reason about which products best match the buyer's intent. Most merchant websites are invisible to them.
This standard defines a lightweight, discoverable format that gives AI agents exactly what they need.
Quick start
Add one line to the <head> of every page on your website:
That tag tells any AI agent or crawler where your structured product feed lives. The rest of this document describes what that feed should contain.
Contents
- What is an agentic feed?
- Why does this standard exist?
- How is it different from a Google Merchant Center feed?
- How do AI agents discover it?
- How do I add it to my website?
- Specification
- Agent Query API
- Examples
- Reference implementation
- Contributing
- Licence
What is an agentic feed?
An agentic feed is a structured product data feed built for AI agents rather than search engine crawlers or human browsers.
A traditional product page is designed to be rendered and read by a person. A Google Merchant Center feed is designed to be parsed by a price comparison engine. An agentic feed is designed to be reasoned about by an AI.
The key difference is intent data. Where a merchant feed tells an agent "here is a cordless drill, it costs £29.99 and it is in stock," an agentic feed tells it "here is a cordless drill that answers the question what drill do I need for assembling flat-pack furniture, solves the problem I keep stripping screws with my old drill, and fits the use case home DIY for a first-time homeowner."
That is the layer of context an AI agent needs to make a confident recommendation to a specific buyer.
Why does this standard exist?
AI shopping is already happening. The tooling to serve it well does not yet exist as an open, portable standard.
Search engines standardised web content discovery through sitemaps, robots.txt, and canonical tags. RSS standardised content syndication through a single autodiscovery tag. Neither was designed for the kind of structured reasoning that AI agents perform when they decide what to recommend.
This specification fills that gap. It defines:
- How a website signals to AI agents that a structured product feed exists
- What format that feed takes
- How intent data (questions, problems, use cases) is structured alongside standard product data
- How AI agents navigate from a buyer's query to a specific product recommendation
The format is intentionally minimal. It builds on schema.org types that crawlers already understand. It adds the intent layer that makes AI recommendation possible.
How is it different from a Google Merchant Center feed?
| Google Merchant Center feed | Agenticfeed | |
|---|---|---|
| Format | XML (RSS-like) | JSON-LD |
| Schema | Google's proprietary spec | schema.org + agenticfeed namespace |
| Primary consumer | Price comparison, Shopping ads | AI agents, LLMs, shopping assistants |
| Discovery | Manual URL submission | Autodiscovery via <link rel="agenticfeed"> |
| Intent data | None | Questions, problems, use cases per product |
| Product context | Price, title, image, availability | Price, title, image + buyer intent layer |
| Attribution | None built in | UTM parameters on every product URL |
A Google Merchant Center feed tells a machine what a product is. An agentic feed tells it why a specific buyer should choose it.
The two are complementary. Many merchants use a GMC feed as the data source for generating an agentic feed.
How do AI agents discover it?
The discovery mechanism follows the same autodiscovery pattern the web has used for decades:
When an AI agent or crawler visits a merchant's website, it reads the page <head>. If it finds a rel="agenticfeed" tag, it knows exactly where to fetch structured product data without being told the URL in advance.
This means:
- No manual registration with each AI platform
- No API keys or access agreements required
- Any AI agent that implements this standard can discover your feed automatically
- The merchant controls the data at their own URL
How do I add it to my website?
Step 1. Add the discovery tag to every page <head>:
Step 2. Serve a JSON-LD feed index at that URL (see examples/feed.json):
Step 3. Serve intent endpoints for each category (see examples/questions.json).
Step 4. Serve product detail documents for each product (see examples/product.json).
For Shopify merchants, agenticfeed.ai handles all of this automatically including injecting the discovery tag into your theme.
Specification
1. Discovery tag
| Attribute | Value |
|---|---|
rel | agenticfeed |
type | application/json |
href | Absolute URL to the feed index document |
Place this tag in the <head> of every page. It must appear in the server-rendered HTML, not injected by JavaScript, so crawlers can find it without executing scripts.
2. Feed index
Content-Type: application/ld+json
Schema.org type: DataFeed
The feed index is the entry point for any agent reading your feed. It identifies the merchant, lists intent endpoints, and provides the URL template for resolving individual products.
See examples/feed.json for a complete example.
Agent workflow:
- Find the discovery tag in the page
<head> - Fetch the feed index
- Read
dataFeedElementto find intent endpoints - Fetch the relevant endpoint based on the buyer's query type
- Match intent entries to the buyer's need and extract product IDs
- Resolve each product ID using the
agenticfeed.resolution.product.urltemplate - Fetch the product detail document
3. Intent endpoints
Intent endpoints are JSON-LD ItemList documents grouped by product category. There are three intent types:
Questions — natural language questions a buyer asks before purchasing.
Schema.org type: Question with suggestedAnswer pointing to the product URL.
See examples/questions.json
Problems — pain points or needs the product addresses.
Schema.org type: ListItem with name (the problem) and url (the product).
See examples/problems.json
Use cases — specific scenarios or goals the product fits.
Schema.org type: ListItem with name (the use case) and url (the product).
See examples/use-cases.json
4. Product detail
Content-Type: application/ld+json
Schema.org type: Product
Each product has its own document at a stable URL. It combines standard schema.org Product data with the intent content under an agenticfeed namespace.
See examples/product.json for a complete example.
| Field | Type | Description |
|---|---|---|
@context | string | https://schema.org |
@type | string | Product |
name | string | Product title |
url | string | Merchant product page URL (UTM-tagged) |
image | string | Primary product image URL |
category | string | Product category |
offers | Object | schema.org Offer — price, currency, availability |
agenticfeed.questions | Array | Questions this product answers |
agenticfeed.problems | Array | Problems this product solves |
agenticfeed.use_cases | Array | Use cases this product fits |
5. UTM attribution
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