MCP (Model Context Protocol)

MCP (Model Context Protocol) is an open standard that defines a common way to connect AI models and agents to external tools, data sources and applications. Instead of building a custom integration for every combination of model and system, MCP proposes a single protocol that both sides understand, the same way a USB-C port replaces a drawer full of proprietary cables.

It was introduced by Anthropic in late 2024 and quickly adopted by much of the AI ecosystem. The core idea is to separate two roles: an MCP server exposes capabilities (query a database, read files, call an API) and an MCP client (the model or the application that hosts it) discovers and uses them in a standardized way. That way, a tool written once becomes available to any compatible agent.

In practice, MCP is the plumbing that connects an AI agent to the real world of a company's systems. When that world is Salesforce and the rest of the corporate stack, MCP is one of the pieces that enables that conversation, part of what gets organized when designing the Salesforce integration.

The problem MCP sets out to solve

Before MCP, connecting a language model to a company's systems was handcrafted work. Each team wrote its own "glue": an ad hoc connector for the CRM, another for the database, another for the ticketing system. If you switched models or providers, much of that work was thrown away. It is the classic M-by-N combinatorics problem: with M models and N tools, you end up maintaining M times N different integrations, each with its own authentication logic, format and error handling.

MCP turns that problem into M plus N. Each model speaks MCP once and each tool exposes MCP once; the protocol in the middle lets every piece understand the others. That is why it is described as the "USB-C of AI applications": a universal connector that replaces the tangle of proprietary cables.

How it works, in simple terms

MCP defines a client-server architecture with three pieces:

• Host: the application that uses AI (for example an assistant, an IDE or an agent platform). It contains the client.
• MCP client: the component that maintains the connection and translates between the model and the server.
• MCP server: the program that exposes specific capabilities of an external system (Salesforce, a database, Google Drive, an internal API).

The protocol standardizes three types of capability a server can offer: tools (actions the model can execute, such as creating an opportunity or checking a balance), resources (read-only data that provides context, such as a document or a record) and prompts (reusable instruction templates). The model discovers these capabilities in real time and decides when to invoke them through tool calling.

Why it matters to a business

MCP's value is not technical in itself, but strategic: it drastically cuts the cost and time of connecting AI to the data where the business actually lives. An AI agent without data access can only talk; with MCP it can read the real status of an order, update an opportunity in the CRM or trigger a flow. And because it is a standard, it avoids lock-in: if you switch models tomorrow, the MCP servers you built still work.

For a company in Argentina or Latin America that already runs its operation on Salesforce, this translates into something very concrete. Picture a consumer goods distributor whose sales team asks over WhatsApp: "How much stock of this SKU do we have, and when does the next replenishment arrive?". With one MCP server that exposes inventory and another that queries Salesforce, the agent builds the answer by combining both sources, without anyone having programmed that specific question in advance. The same plumbing then serves collections, sales forecasting or support.

MCP compared with other ways to integrate

MCP does not replace APIs or earlier approaches: it builds on them and adds a standard layer designed to be consumed by a model. It helps to place it next to its close relatives:

The key difference from classic tool calling is portability: tool calling describes the mechanism by which a model decides to call a function, while MCP standardizes how those functions are published and discovered so they serve any compatible model. And unlike RAG, which focuses on retrieving text to provide context, MCP enables both reading and acting on live systems.

Common mistakes when thinking about MCP

• Believing MCP "is the AI": it is not. It is the connection; the reasoning still happens in the LLM.
• Exposing an MCP server without controls: every tool you enable is a real action the model can execute, so authentication, permissions and guardrails are as important as in any integration.
• Assuming it replaces your API: in general it wraps it, it does not eliminate it.

In short, MCP is the standard that is turning AI agents from conversational demos into operators connected to the company's real systems, with less hand-glued code and more reuse.