Quick definition
MCP (Model Context Protocol) is an open standard, introduced by Anthropic in November 2024, that defines a common way for language models and AI agents to connect with external tools, data, and systems. Instead of building a custom integration for every combination of model and system, MCP lets any compatible model use any compatible tool through the same protocol.
What does it mean?
Before MCP, connecting an AI model to an external system—a product catalog, a CRM, a database—required writing an integration specific to that exact combination of model and system. If a company wanted three different models to use five different tools, it could end up building as many as fifteen separate integrations, each with its own code and maintenance.
MCP solves that fragmentation problem, and a useful analogy captures it: it works like a USB-C port for AI applications. Just as USB-C standardized how any device connects to any cable without proprietary adapters, MCP standardizes how a model discovers which tools are available to it, what each one does, and how to invoke them—without every model-and-system combination requiring an integration built from scratch.
An MCP server exposes a set of "tools" (functions the model can invoke) and "resources" (data the model can query) from a specific system—for example, an ecommerce catalog or an OMS's inventory. Any MCP-compatible model or agent can connect to that server and use its capabilities without knowing the system's internal implementation details.
Why it matters
Fragmented integration between AI models and business systems was, until 2024, the main bottleneck to scaling AI agents beyond isolated demos: every new model-and-tool combination demanded dedicated engineering work. MCP solves that problem with a common protocol, which has enabled the community to build thousands of reusable MCP servers for popular systems, and has made the standard, in practice, the predominant way to connect agents to tools.
For digital commerce, this is directly relevant: a well-built MCP server on top of a platform's catalog or checkout engine allows any compatible shopping agent—regardless of which company developed it—to operate on that store without a custom integration.
How it works
MCP defines two roles: the client (typically an AI model or agent) and the server (the system exposing capabilities). The MCP server publishes a catalog of available tools—with their names, descriptions, and expected parameters—and of queryable resources. The client, upon connecting, automatically discovers what that server can do and decides, based on the user's goal, which tool to invoke and with which arguments.
Communication follows a standardized format (based on JSON-RPC), which means any client and server implementation that follows the specification is interoperable, without coupling to a specific model or infrastructure vendor.
Applied example in AI Commerce
A marketplace exposes its catalog, inventory, and quoting engine through an MCP server. A customer's B2B purchasing agent—built on a model from a different provider than the marketplace's—connects to that server, discovers it can check availability and generate a quote, and executes both actions to complete a recurring purchase, without the marketplace having built an integration specific to that particular agent.
Related concepts
MCP is the connection mechanism an AI Agent uses to invoke tools and query external data. It depends on an API First architecture behind the MCP server, since in practice an MCP server is usually a layer that translates MCP calls into existing API calls. It is a core technical piece of Agentic Commerce, and it complements RAG when the goal is not just to retrieve information but also to execute actions on a system.
Common mistakes
Confusing MCP with an AI model itself: MCP is not a model, it is a communication protocol between models and external systems. Assuming any API automatically becomes MCP-compatible: it requires explicitly building an MCP server that exposes that API according to the protocol's specification. Finally, underestimating the importance of each tool's description: if the description is ambiguous or incomplete, the model can invoke it incorrectly, even if the underlying API works well.
The Edgebound Labs perspective
In the lab we treat an MCP server with the same rigor as any public API: clear documentation, explicit permission boundaries, and tests of what happens when an agent invokes a tool with unexpected data. The "USB-C port" analogy is useful for explaining the concept, but it doesn't replace the discipline of designing every exposed tool with an eye to who—or which agent—will use it, and with what margin for error.
Frequently asked questions about MCP
Who created MCP?
Anthropic introduced it as an open standard in November 2024.
Is MCP the same as an API?
Not exactly. MCP is a protocol that standardizes how a model discovers and uses tools and data, generally built on top of existing APIs.
Do I need to rewrite my APIs to use MCP?
No. You build an MCP server that exposes your existing APIs according to the protocol's specification, without rewriting them from scratch.
Does MCP work with any AI model?
It is designed as an open standard, compatible with any model or client that implements the specification—not exclusive to one provider.
What is an "MCP server"?
It is the implementation that exposes a specific system's tools and resources—for example, an ecommerce catalog—for a model or agent to use.
Does MCP solve AI agent security?
It defines the communication protocol, but security (permissions, action limits) depends on how the server is implemented and what controls are added on top of it.
Keep exploring the glossary
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