Introduction
In the age of AI agents that browse, compare, and complete purchases autonomously, AI product discoverability is no longer a buzzword — it’s a survival tactic. Traditional SEO and rigid schemas simply won’t suffice when models act as first-touchpoints between customers and your catalogue. To compete, brands must build Model Context Protocols (MCPs): structured interfaces that let AI systems query, filter, and transact with your product catalogue in real time. This article walks you through how to evolve from schemas to MCPs, so your products remain visible — not just to humans, but to the intelligent agents of tomorrow.
Why AI Product Discoverability Matters in Agentic Shopping
Today’s LLM-based agents (e.g. next-generation assistants) are beginning to perform agentic shopping — meaning they don’t just suggest products; they actually traverse product catalogs, compare options, and place orders on behalf of users. The difference between being “findable by AI” and simply “findable by humans” is growing fast.
While SEO and schema markup are still necessary, they are no longer sufficient. AI agents can’t reliably depend on crawling your pages or parsing HTML. Instead, they benefit from live, structured access to your catalog — which is where AI product discoverability becomes essential.
Brands that merely mirror their web catalogs into schema or feeds risk being filtered out by agents that prioritise real-time relevance, semantics, and transaction capabilities. If your competitors expose MCPs while you don’t, their catalog may show up in more agent-led shopping journeys — leaving you behind.
The Role of Schemas, Structured Data & Metadata
Before you build an MCP, your foundation must be solid. This means using established standards like schema.org, structured product feeds (e.g. Google Shopping feeds), and rich metadata tags (e.g. brand, material, dimensions, tags).
Yet, those schemas are inherently read-only. They tell search engines what your product is, but don’t allow dynamic querying, filtering or transaction actions by third parties. AI agents need more than static declarations — they need interfaces.
Consider this analogy: schema markup is a shop window; an MCP is a concierge desk where agents can ask for stock, compare, and even transact.
To bridge the gap:
Use rich metadata in schema (e.g.
additionalPropertyin schema.org) to capture brand attributes, materials, sustainability, etc.Ensure your product feed is kept up-to-date (real-time or near-real-time).
Expose feeds via APIs or endpoints that can serve machine clients.
Use consistent identifiers (SKUs, GTINs, identifiers) so mapping is reliable.
Even when AI agents do web crawling, they’ll first surface your schemas — so that layer must be clean, comprehensive, and aligned with your deeper MCP metadata design.
Designing an MCP: Core Components & Best Practices
To build an MCP that’s not just functional but future-resilient, you need to consider several architectural and design decisions.
1. Manifest / Capability Declaration
Your MCP should publish a manifest file (e.g. JSON) that declares what the API can do: search, filter, availability, purchase, etc. Agents use this to understand your endpoints before calling.
2. Query / Search Endpoints
These allow agents to ask “find me products matching X, Y, Z” with filters, ranking, and pagination. Use semantic ranking or embedding-based retrieval to support fuzzy queries.
3. Metadata & Semantic Context
Expose structured metadata fields and context layers (e.g. “sustainability”, “material”, “brand ethics”). Embedding vectors or semantic tags help agents interpret meaning beyond literal matches.
4. Transactional / Action Endpoints
If you want to enable agents to complete purchases, offer endpoints like addToCart, checkout, applyDiscount, confirmOrder. Strong authentication and security are mandatory here.
5. Security, Permissions & Rate Limiting
You’ll need scopes, tokens, throttling to protect your systems. Agents should only see what they’re permitted (e.g. currency, regional stock, promotions).
6. Fallback / Human Path Support
Not all agents will support MCPs. Offer fallback behavior (e.g. fallback to your web site or feed links) for older clients.
7. Versioning & Backwards Compatibility
As you iterate, ensure older clients (or agent versions) still operate. Version your API and manifest standards.
Best Practices
Start with read-only (search, filter) before enabling transactional capability
Log all agent requests for analytics
Use embedding-based ranking to complement keyword filters
Provide clear error messages / fallbacks so agent logic can recover gracefully
Conclusion & Call-To-Action
In a world where AI agents are becoming first-class shoppers, building for AI product discoverability is no longer optional — it’s essential. By evolving from static schemas to fully functional MCPs, brands can ensure their catalogues remain visible, queryable, and actionable in agentic shopping flows.
If you’re ready to get started, your next step is to pick a small product subset, define your manifest, build read-only endpoints, and test them with simple agents. Over time, you can expand to full transactional capabilities, monitor agent traffic, and iterate.
Call to action
If you’d like help defining your MCP manifest or comparing tradeoffs, let me know — I can help you build a lean MVP blueprint you can prototype in weeks, not months.
FAQs
1. What is an MCP?
A Model Context Protocol (MCP) is an interface allowing AI models to interact with your system — querying, filtering, and acting — beyond mere reading of HTML or schema metadata.
2. How is it different from schema markup?
Schema markup is read-only data for search engines. MCPs support dynamic queries and (optionally) transactions.
3. Will all AI agents support MCPs now?
Not yet — it’s emerging. But early adopters (e.g. Shopify’s MCP initiative) are paving the way.
4. Is there a standard MCP yet?
There are emerging protocols and proposals (e.g. Shopify MCP), but the space is evolving.
5. When should a brand invest in MCP vs sticking to SEO/feeds?
Start with SEO & schema; once your catalog and tech maturity allow, phase in MCP for product lines where AI visibility matters most
