MCP vs UCP: Understanding their roles in AI-driven commerce

What is the Model Context Protocol (MCP)?

Google Cloud defines the Model Context Protocol as an open standard that specifies how AI models communicate with external systems. MCP provides a consistent way for AI agents to retrieve information and invoke actions across tools, services, and data sources, rather than operating in isolation.

McKinsey places MCP within a broader shift toward agentic AI, in which agents handle multistep tasks spanning systems and time. As agents move between tools, they need a way to retain context, intent, and prior activity. MCP supports this by enabling structured communication across models and tools, instead of relying on static prompts or one-off API calls that reset context at every step.

From a developer perspective, MCP standardizes how LLM-based applications connect to external functions and services. That standardization becomes increasingly important as teams adopt generative AI to coordinate workflows that depend on multiple tools, data sources, and execution environments. Rather than embedding logic directly into prompts, MCP separates reasoning from execution, allowing agents to request information or perform actions through a defined interface.

What problems does MCP intend to solve?

Once AI agents move beyond single responses and start working across tools and systems, predictable issues show up in how models access information and carry work forward.

Common issues include:

• AI models don’t automatically have access to external data sources, applications, or services, which limits their ability to retrieve current information or act beyond the model itself
• Static prompts and isolated API calls fail to preserve context, intent, and prior activity across multistep tasks
• Developers lack a standardized way to connect LLM-based applications to tools and function calls across systems
• Multitool workflows break down when agents cannot share state or coordinate actions across environments 

What is the Universal Commerce Protocol (UCP)?

Google defines the Universal Commerce Protocol as an open-source standard designed to support agentic commerce by establishing a common language and shared functional primitives across consumer surfaces, business systems, and payment providers. UCP is built to work with existing retail infrastructure, allowing commerce capabilities to be exposed to AI agents without replacing current platforms.

UCP focuses on execution. Google explains that the protocol standardizes how commerce functions, such as product discovery, cart creation, checkout, order management, and payments are made available to AI agents. Instead of requiring custom integrations for every new interface, UCP provides a single abstraction layer that enables agents to interact with commerce systems consistently across environments, including AI Mode in Search, the Gemini app, and future AI-driven surfaces.

From an ecosystem standpoint, UCP is designed for flexibility rather than lock-in. Businesses retain control over their business logic and remain the merchant of record, while choosing how to integrate through APIs, Agent-to-Agent communication, or MCP. 

Payment providers participate through a modular, security-first design that supports verifiable user consent and interoperable payment methods. This approach reflects how many teams are starting to use AI for e-commerce, where discovery and transactions increasingly happen inside conversational and assistant-led experiences.

What problems does UCP intend to solve?

As commerce activity moves into conversational and agent-led contexts, a different set of practical issues emerges.

Common issues include:

• Commerce platforms require bespoke integrations for each consumer surface, creating an N×N integration bottleneck
• Real-time inventory checks, dynamic pricing, and transactions are difficult to support within conversational experiences using traditional infrastructure
• Businesses lack a standardized way to expose discovery, checkout, order management, and payment capabilities to AI agents
• Payment handling and user consent vary across platforms, limiting interoperability across agent-driven flows

MCP vs UCP: How do their roles differ in your AI commerce stack

Once MCP and UCP are understood on their own terms, the difference between them comes down to where each one operates and what problems it addresses. One governs how AI agents understand and carry context. The other governs how commerce systems expose and execute transactions. They solve related problems, but they don’t overlap.

The table below shows how their roles differ in practice.

Viewed together, MCP supports decision continuity across AI workflows, while UCP supports transaction execution across commerce systems. Both are required when AI agents move from assisting shoppers to acting on their behalf.

Why does product data become the shared dependency for both MCP and UCP?

When both protocols are in place, they rely on product data that AI agents can use without clarification. MCP allows agents to carry context and intent across tools. UCP allows those agents to execute commerce actions across platforms. Neither protocol defines the product information itself, which is why PIM for modern e-commerce becomes a foundational requirement rather than a supporting system.

Before an agent can act, it needs clear answers to basic questions. Which product applies in this situation? Which attributes matter for comparison? Which variant is available right now? Which business rules constrain pricing, fulfillment, or eligibility? Product data supplies those answers. Protocols handle communication and execution, but they cannot compensate for missing, inconsistent, or loosely defined product information.

Product information management (PIM) takes on a different responsibility here. Product data stops serving only human browsing and channel publishing and starts supporting automated decision-making. AI agents rely on explicit values, defined relationships, and governed attributes rather than interpretation, which is why many teams now look to AI-powered PIM as part of their broader approach to scaling automation across commerce workflows.

How Inriver prepares product data for MCP- and UCP-enabled commerce

When AI agents begin working directly with your product data, maintaining structured and governed product information across systems becomes an operational requirement. Inriver supports this through managed product models, attributes, relationships, and validation rules designed for downstream use, including AI for product content creation and other agent-driven workflows.

Key Inriver capabilities that support MCP- and UCP-enabled commerce include:

• Structured product models
  Products, variants, attributes, and relationships are explicitly defined, allowing downstream systems to resolve which product applies, which attributes matter, and how variants relate to availability, pricing, or fulfillment rules. This structure also supports AI enrichment by providing AI systems with clear, governed inputs rather than free-text fields.
  
  
• Normalized and consistent product data
  Attributes, classifications, and taxonomies remain aligned across use cases. This consistency reduces ambiguity when product information is used across discovery, comparison, and transaction workflows.
  
  
• API-first data access
  Product data is exposed through APIs designed for downstream systems and AI workflows. This approach aligns with how MCP connects agents to external tools and how UCP integrates commerce capabilities across platforms, without duplicating or reshaping data for each surface.
  
  
• Separation of core data from channels
  Core product information is maintained independently of where it is executed or displayed. The same product records can support conversational discovery, comparison logic, and transactional flows without rewriting data for each interface.
  
  
• Built-in governance and validation
  Validation, versioning, and controlled updates ensure changes to product attributes or classifications propagate consistently across connected systems, which becomes critical once agents act autonomously.

Prepare your commerce stack for AI agents

If AI agents already influence how products get discovered and purchased in your channels, preparing for what comes next means looking past interfaces and focusing on how your systems work together. Agent-led commerce relies on coordination across APIs, interoperable data, trust frameworks, and governance within an agentic commerce ecosystem that connects platforms, services, and execution layers.

Your product information plays a central role in this setup. MCP and UCP give agents ways to understand context and complete transactions, yet neither defines the product data on which those decisions depend. 

PIM provides the structure, governance, and consistency required for automated decisions to work without constant manual correction. 

See how your product data can support AI-driven commerce in practice. Schedule a demo to understand how a PIM can prepare your stack for agent-led execution.