MCP (Model Context Protocol)
An open standard developed by Anthropic that defines how AI models connect to external tools, data sources, and services, providing a consistent interface for capabilities like reading data, calling functions, and using reusable prompt templates.
What It Is
MCP solves a fragmentation problem. Before MCP, every AI application had to build its own integrations: custom code to connect an LLM to a CRM, a different custom integration to connect it to an ERP, and a third for a document repository. Each integration was bespoke, brittle, and had to be rebuilt when the underlying model changed.
MCP standardizes this interface. An MCP server exposes a set of capabilities: resources (data the model can read), tools (functions the model can call), and prompts (reusable instruction templates). Any MCP-compatible AI client can connect to any MCP server and use those capabilities without custom integration work.
The analogy is USB: before USB, every peripheral had its own connector. USB standardized the interface so that any device could connect to any USB port. MCP does the same for AI and enterprise systems.
In B2B Commerce Context
For enterprise B2B operators, MCP matters because it reduces the integration tax on AI adoption. Building AI capabilities on top of OroCommerce typically requires integrating with ERP, CRM, pricing engine, and logistics systems. If each of those systems exposes an MCP server, an AI application built on a standard MCP client can connect to all of them without bespoke middleware.
The vendor-independence benefit is significant. AI applications built on MCP can switch underlying model providers without rebuilding integrations. OroCommerce can adopt a better model when one becomes available without touching the integration layer connecting it to ERP and CRM. The protocol separates the intelligence from the plumbing.
MCP also provides a clean security boundary. The MCP server owner decides exactly what resources and tools to expose to AI clients. Sensitive data that should not be accessible to AI agents simply does not appear in the server’s capability manifest. This makes authorization auditable and controllable at the protocol level.
When You Need It
- You are building AI applications that need to connect to multiple enterprise systems and want to avoid rebuilding integrations for each model or client upgrade.
- You want a standardized way to expose your platform's capabilities to AI agents without writing custom connectors.
- Your AI ecosystem includes multiple tools (orchestration frameworks, client applications, models), and you want them to share integration infrastructure.
- You are evaluating AI vendor lock-in risk and want integration investments to be portable across model providers.
Check whether your key enterprise systems (ERP, CRM, commerce platform) already offer or are building MCP servers. The ecosystem is growing rapidly. Many major enterprise software vendors announced MCP support in 2025.
What It Is Not
- MCP is not a model or an AI capability itself. It is a protocol, a communication standard. It does not make AI smarter, faster, or more accurate. It makes AI easier to integrate with enterprise systems in a standardized, maintainable way.
- It is not a replacement for function calling. MCP is an architecture for exposing tools and resources. Function calling is the mechanism by which a model decides to use those tools within a conversation. MCP and function calling work together: MCP defines what tools are available; function calling is how the model uses them.
- It is not yet universally adopted. As of 2025, MCP is gaining rapid adoption in the AI tooling ecosystem, but not all enterprise systems have MCP servers. For systems without native MCP support, custom integration work is still required. MCP just standardizes what that integration looks like for the AI client side.
Comparison
| MCP concept | What it means in practice |
|---|---|
| MCP server | An enterprise system (ERP, CRM, commerce platform) that exposes capabilities via MCP |
| MCP client | An AI application that connects to MCP servers to use their capabilities |
| Resources | Data the model can read, product records, customer accounts, order history |
| Tools | Functions the model can call, create order, check inventory, update record |
| Prompts | Reusable instruction templates exposed by the server for common tasks |
See also
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