What is MCP (Model Context Protocol) and what is it used for in your company?

July 14, 2026 By Miguel Alvarez

Artificial intelligence agents only add value if they can access the systems where the business happens: the ERP, the CRM, databases, internal documentation. Until recently, each of those connections required a custom integration—expensive and difficult to maintain. The Model Context Protocol (MCP) has come to solve that problem and, in just over a year, has become the standard used by all major artificial intelligence providers.

In this article, we explain what MCP is, why it has become established so quickly, and what it can do for your company.

The standard that connects AI agents to your company’s systems

Model Context Protocol is an open standard that defines how a model or AI agent connects to external systems, whether corporate applications, tools, or data sources. It was created by Anthropic, the company behind Claude, in November 2024, and from day one it was released as open source so anyone could adopt it.

The most common comparison is USB-C. Before a universal connector existed, each device needed its own cable. The same was true for AI agents. Each assistant required a different integration for each system, and the result was a patchwork of connections that was impossible to scale.

MCP works with a client-server model. An MCP server exposes a system’s capabilities—for example, checking stock in the ERP or searching documentation. An MCP client, typically an AI agent or assistant, uses those capabilities when needed. Once the server for a system has been developed, any compatible agent can use it, whether from Anthropic, OpenAI, Google, or Microsoft.

From an Anthropic protocol to an industry standard

The key moment came on December 9, 2025, when Anthropic donated MCP to the Agentic AI Foundation, a foundation created under the umbrella of the Linux Foundation and launched together with OpenAI and Block, with support from Google, Microsoft, AWS, Cloudflare, and Bloomberg. Since then, MCP has not been a single vendor’s protocol, but open, neutral infrastructure governed by the community.

The figures show the speed of adoption. In its first year, MCP surpassed 97 million monthly downloads of its SDKs and 10,000 active public servers. Today it has native support in Claude, ChatGPT, Gemini, Microsoft Copilot, Cursor, and Visual Studio Code, among others.

For a company, this trajectory has a practical takeaway: betting on MCP does not lock you into any vendor. What you build today will remain valid even if the AI model using it changes tomorrow.

What MCP is used for in a company

The theory is easier to understand with concrete cases. These are some of the uses already being implemented.

Customer service with real data: An agent that checks orders, stock, or delivery times directly in the ERP and responds with up-to-date information.
Assistants for internal documentation: Procedures, contracts, technical manuals, or policies, accessible via natural language and with source traceability.
On-demand reporting and analysis: Agents that read from the data warehouse or BI platform and prepare summaries, comparisons, or alerts.
Process automation: Agents that create tasks, update records, or trigger notifications in corporate tools as part of a workflow.

In all cases, the key is not the chat. It is governed access to the organization’s systems and data, with defined permissions and control over what the agent can and cannot do.

What changes compared to traditional integrations

Without a standard, connecting three AI assistants to five corporate systems requires fifteen different integrations. Each new tool multiplies the work, and each vendor change forces you to rebuild it.

With MCP, each system is connected only once. The ERP’s MCP server works equally for a procurement agent and a customer service agent, and it will still work if a new assistant is added next year. This translates into lower development costs, less maintenance, and much faster pilots, while also reducing dependence on a single technology vendor.

Security and data governance: the other half of the project

Having a universal connector does not mean everything should be connected. A serious MCP project requires deciding what information is exposed, to whom, and with what permissions, logging agent activity, and ensuring the data they consume is reliable.

That is why agent projects work best when they are built on a solid foundation of data governance. Defining owners, quality, and access is not a preliminary formality—it is what ensures the agent responds correctly and the organization stays in control. It is also advisable to keep the regulatory framework in mind, with the AI Act transparency obligations applying from August 2026.

How to get started: three sensible steps

You do not need a major corporate program to benefit from MCP. A sensible starting point includes three steps:

First, choose a well-scoped use case with measurable value, for example querying internal documentation or handling orders.
Second, inventory the systems and data sources involved and decide which capabilities are exposed via MCP and with what permissions.
Third, run a pilot in a controlled environment, measure results, and scale based on what you learn.

MCP and the Claude Partner Network: how we apply it at LIS

At LISDATA, we work with MCP in real-world AI agent projects. We are part of Anthropic’s Claude Partner Network, the company that created the protocol, with a team specialized in AI agents and certified by the tech giant. This closeness to the technology’s origin allows us to apply best practices from the design stage, rather than discovering them along the way.

We cover the full project lifecycle, from identifying use cases to go-live and ongoing governance, integrating agents with each organization’s systems, applications, and data sources. Projects such as the document chat developed together with INNOVA (LAE Group) show how this way of working translates into measurable results.

If you are considering how to connect artificial intelligence with your company’s systems, let’s talk.

Frequently asked questions (FAQ) about MCP

We address the most common questions about MCP, its security, and how to deploy it in enterprise environments.

What does MCP stand for, and who created it?

MCP stands for Model Context Protocol, an open standard that defines how AI models and agents connect to external systems and data sources. It was created by Anthropic in November 2024, and since December 2025 it has belonged to the Agentic AI Foundation, under the Linux Foundation.

Does MCP only work with Claude?

No. Although it originated at Anthropic, it is an open standard with support across the leading platforms, including ChatGPT, Gemini, Microsoft Copilot, and Cursor. An MCP server developed today works with any compatible client.

Does my company need to develop its own MCP servers?

It depends on the system. There are thousands of public servers for widely used tools. For proprietary systems, such as a custom ERP or an internal database, a specific server is developed that exposes only the necessary capabilities.

Is it safe to connect my systems to an agent via MCP?

MCP makes it possible to define precisely what the agent can do and which data it can access. Security depends on the project design. Per-user permissions, action traceability, and controlled environments are part of any well-executed implementation.

What is the relationship between MCP and AI agents?

MCP is the pathway through which an agent accesses external tools and data. Without that access, an agent is limited to conversation. With it, it can query systems, execute actions, and operate within the company’s processes.