MCP vs. ACP: What’s the Difference, and When Should Each Be Used?
Artificial intelligence is changing how people interact with data, products, and content. But as organizations integrate generative AI into enterprise search and ecommerce, many encounter a critical question: what’s the right protocol for my use case?
Two emerging frameworks are shaping how AI systems connect, communicate, and deliver meaningful, contextual results — MCP (Model Context Protocol) and ACP (Agentic Commerce Protocol). Understanding how they differ, and when each is best applied, is key to building scalable, safe, and high-performing AI-driven experiences.
This blog unpacks MCP vs. ACP, how each fits into enterprise AI architectures, and how Lucidworks’ platform can help organizations take advantage of both for commerce and beyond.
The Rise of AI Protocols: Why They Matter
As generative AI applications move from experimentation to production, the conversation has shifted from “Can we build an AI model?” to “How can AI interact intelligently with our data, systems, and users?”
That’s where AI protocols come in. Think of them as the rules and connectors that define how AI models and agents share information, maintain context, and produce results within an organization’s ecosystem.
Without protocols, enterprise AI can become fragmented — models operate in silos, integrations break, and governance becomes difficult. Protocols like MCP and ACP bring structure to the chaos, enabling models, tools, and workflows to interact seamlessly and responsibly.
Defining MCP and ACPz
| Protocol | Full Name | Primary Purpose | Common Use Cases | Ideal Environment |
|---|---|---|---|---|
| MCP | Model Context Protocol | Defines how AI models exchange and maintain context | Generative AI orchestration, model chaining, retrieval-augmented generation (RAG) | Enterprise AI systems, data-rich organizations |
| ACP | Agentic Commerce Protocol | Tailors agentic AI to commerce-specific contexts like product discovery and recommendations | Ecommerce, merchandising, retail personalization | Commerce-driven enterprises |
What is MCP (Model Context Protocol)?
MCP defines how AI models share and manage context — the information that ensures each model in a pipeline “knows” what’s relevant at any given time.
For example, a customer support chatbot might use several specialized models:
- One for sentiment detection
- One for product lookup
- One for response generation
MCP ensures these models exchange data effectively so the system behaves like a single, unified intelligence rather than three disconnected bots.
In technical terms, MCP focuses on:
- Context propagation: Keeping track of state and user intent across models or tools
- Interoperability: Allowing multiple AI systems (LLMs, RAG models, vector databases) to talk using shared formats
- Governance: Defining permissions and visibility into how data and context are used
In Practice: MCP in Enterprise AI
Imagine a B2B company that wants to integrate a generative AI assistant into its internal knowledge base. Employees ask complex questions like:
“Show me which suppliers have the highest on-time delivery rate over the past two years.”
An MCP-enabled architecture lets the assistant:
- Retrieve data from ERP and analytics systems
- Pass contextual summaries to a language model
- Generate a secure, compliant natural-language answer
Each step maintains context and control — vital for enterprises handling proprietary data or regulated content.
In short: MCP is about how AI models talk to each other and keep track of what’s going on.
What is ACP (Agentic Commerce Protocol)?
If MCP defines how AI models interact, ACP defines how AI agents behave — specifically within commerce environments.
ACP, or Agentic Commerce Protocol, extends generative AI’s capabilities into transactional and decision-based workflows like product recommendations, dynamic pricing, or personalized content generation.
Where MCP focuses on model-to-model communication, ACP focuses on goal-oriented action. It establishes rules for agentic behavior in commerce, such as:
- Understanding and enriching product data
- Matching shopper intent with inventory
- Generating or optimizing product descriptions
- Executing actions within a commerce system (e.g., updating listings, sending recommendations)
In Practice: ACP in Commerce AI
Let’s take a retailer with millions of SKUs and limited metadata. Their shoppers frequently search for “summer sandals,” but the product catalog lacks rich, descriptive attributes to surface the right items.
An ACP-powered agent within Lucidworks’ platform could:
- Analyze incomplete product data
- Generate contextualized attributes (“open-toe,” “lightweight,” “leather upper”)
- Feed those enhancements into search indexes and recommendation models
- Continuously learn from user behavior to refine matching accuracy
The result: richer discovery experiences, improved conversions, and higher customer satisfaction — all through AI agents that act with defined rules, context, and control.
In short: ACP is about how AI agents act intelligently and safely within commerce ecosystems.
MCP vs. ACP: Key Differences
| Category | MCP (Model Context Protocol) | ACP (Agentic Commerce Protocol) | Example Use | Lucidworks Application |
|---|---|---|---|---|
| Primary Focus | Context and interoperability | Action and decision-making in commerce | Connecting LLMs and RAG systems for enterprise search | Powering contextual retrieval and AI orchestration |
| Core Function | Coordinates model interactions | Orchestrates agentic behavior for product and shopper data | Enriching product data and personalizing recommendations | Driving intelligent merchandising and agentic product discovery |
| Scope | General-purpose AI systems | Commerce, merchandising, and retail | — | — |
Together, MCP and ACP represent the evolution from generative AI that responds to agentic AI that acts.
When to Use MCP or ACP
Choosing between MCP and ACP depends on your objectives:
✅ Use MCP When:
- You need multiple AI models to work together coherently
- Context continuity is critical across tools and tasks
- You’re orchestrating search, chat, or knowledge workflows in enterprise environments
- You want to ensure explainability and governance across AI systems
Example: A financial institution deploying an internal assistant that answers regulatory questions. MCP ensures every data source and model exchange happens transparently and consistently.
✅ Use ACP When:
- You’re operating in ecommerce or merchandising
- Product and customer data need ongoing enrichment and optimization
- You want AI agents that act — not just respond — within business rules
- You’re focused on conversion, personalization, or real-time recommendations
Example: A large retailer uses ACP-driven agents within Lucidworks to dynamically rewrite product titles and descriptions for SEO, improving organic traffic and clickthroughs.
How Lucidworks Supports Both
Lucidworks has long focused on connecting data, models, and user intent — the foundation of MCP and ACP alike.
- For MCP use cases: Lucidworks’ platform integrates with multiple LLMs and RAG systems to provide context-aware retrieval, ensuring AI assistants and enterprise search solutions understand both the question and the underlying business data.
- For ACP use cases: Lucidworks applies agentic AI to commerce — enriching product catalogs, personalizing search results, and enabling agents to act with contextual awareness and control.
The result is a flexible, future-ready platform that bridges model context and agentic commerce — helping organizations get the most out of their AI investments.
Why the Difference Matters
The distinction between MCP and ACP isn’t academic — it’s operational.
Enterprises that adopt the right protocol for their use case will see major advantages in scalability, reliability, and governance.
Misalignment, on the other hand, can lead to:
- Disconnected AI systems that lose context between tasks
- Commerce agents acting outside defined parameters
- Difficulty scaling from prototype to production
As generative and agentic AI evolve, organizations will increasingly need both: MCP for context and interoperability, ACP for action and intelligence in commerce.
Lucidworks is already enabling that convergence.
Summary Table: MCP vs. ACP in Enterprise AI
| Use Case | Protocol | AI Function | Example Outcome |
|---|---|---|---|
| Knowledge assistant for enterprise data | MCP | Context propagation across models | Consistent, explainable responses |
| AI product enrichment engine | ACP | Agentic action for catalog optimization | Richer descriptions, better SEO |
| Internal workflow automation | MCP | Model orchestration and governance | Secure, compliant AI pipelines |
| Personalized commerce search | ACP | Adaptive recommendations and product discovery | Higher conversion and engagement |
The Future: MCP + ACP Together
The next generation of enterprise AI will blend both protocols. Imagine a hybrid system where MCP governs context between large language models while ACP executes agentic commerce decisions downstream.
For example, a future Lucidworks deployment could:
- Use MCP to understand shopper intent from conversational queries
- Pass structured context to ACP agents that adjust product listings or recommendations
- Continuously refine results using behavioral feedback loops
That’s the promise of enterprise-grade, context-aware, agentic AI — and it’s already coming to life.
Key Takeaways
- MCP (Model Context Protocol) manages how AI models share and maintain context — critical for interoperability and governance.
- ACP (Agentic Commerce Protocol) defines how AI agents act in commerce environments — enriching, optimizing, and personalizing at scale.
- Use MCP for orchestration and enterprise search; ACP for product discovery, merchandising, and personalization.
- Lucidworks supports both approaches — connecting data, intent, and action through intelligent search and AI enrichment.
- The future of enterprise AI will blend MCP + ACP, enabling both contextual understanding and agentic action.