Qué es un MCP (Model Context Protocol) y por qué importa en el flujo de trabajo de los desarrolladores

In recent years, working with language models has often meant improvising integrations through APIs, SDKs, and libraries. They worked, but many times they felt more like makeshift patches than solid engineering.

This raises the question: what is MCP and how can it improve this scenario?

The Model Context Protocol (MCP) emerges as an open standard that organizes the connection between LLMs, repositories, and tools. In other words, the MCP model context protocol is the answer to a common problem: how can a language model truly understand the real context of a project without relying on endless manual integrations?

What is MCP (Model Context Protocol)?

MCP is a protocol designed to give language models structured access to external contexts. It is not a model, not a framework, and not just another library to add to the list.

The key lies in its function: an MCP is a customized solution that can expand and adapt to new needs. The protocol organizes how an LLM accesses repositories, databases, development tools, or enterprise systems in a structured way, making it easier for those initial integrations to evolve and remain sustainable without needing to be rebuilt from scratch.

Put simply, MCP establishes a common language between the model and its environment.

MCP Architecture and Key Concepts

The architecture of MCP is built on four main components:

1. Server – exposes resources such as a code repository, an API, or a database.
2. Client – consumes those resources; it can be an IDE like VSCode, a CLI application, or an AI agent.
3. Tools – define the instructions or capabilities that the LLM can use within the protocol, essentially specifying which functions are available for execution.
4. Transport – determines how the client and server communicate. It is not limited to JSON-RPC; different protocols can be used depending on system requirements.

This modular design means that an MCP is a customized solution that can be expanded and adapted over time. It organizes how an LLM interacts with external systems, repositories, databases, or enterprise tools, so that initial integrations can evolve without needing to be rebuilt from scratch.

Practical use cases

In VSCode

MCP enables an editor that truly understands the entire project:

• Autocompletion with knowledge of the whole repository.
• Refactoring that considers real dependencies.
• Unit test generation based on the project’s existing logic.

In the CLI

A simple example would be:

mcp run "List the endpoints defined in this repository"

Here, the model doesn’t guess or hallucinate—it accesses the repo via the MCP server and provides a reliable response.

Integration with agents and tools

Agent frameworks like LangChain or Semantic Kernel often face the same obstacle: they require custom connectors for every system. MCP changes this dynamic.

With the protocol in place, an agent can query a repository, a support database, and a CRM—without needing a different connector for each one. The result is less glue code and a cleaner, more maintainable flow.

End-to-End Example

A real workflow with MCP might look like this:

• An MCP server is configured to expose a repository.
• The server connects to a VSCode client.

A request is made:

"Generate an integration test for the endpoint /users/create"

• Through MCP, the model accesses the repo, identifies the endpoint, and returns a ready-to-run test.

In this scenario, the LLM doesn’t improvise, it works with the project’s actual context.

Key benefits

• Efficiency: less time spent building manual integrations.
• Standardization: a common framework that simplifies interoperability.
• Scalability: adaptable to projects of any size.
• Future compatibility: being open, MCP ensures integration with tools that don’t even exist yet.

Best practices and considerations

Adopting MCP requires keeping some points in mind:

• Clearly define which resources are exposed to prevent security risks.
• Remember that the protocol organizes access but doesn’t replace designing a safe, efficient workflow.
• Ensure client and server versions remain aligned to avoid compatibility issues.

MCP: From patchwork to a solid standard

MCP represents a turning point in how developers integrate language models into their workflows. It leaves behind ad-hoc solutions and introduces a standard that reduces friction, eliminates duplication, and enables robust, sustainable integrations.

Adopting it in environments like VSCode or the command line not only improves immediate productivity but also lays the foundation for a more interoperable and reliable AI ecosystem.

In a landscape where most solutions feel like temporary fixes, the Model Context Protocol MCP is the proposal that turns improvisation into solid engineering.
 

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