Model Context Protocol (MCP): an open standard for connecting AI to data and tools

Artificial intelligence is progressing rapidly, but to be truly useful, an AI agent cannot simply generate text. It must also be able to access data, interact with files, or even call external services.

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That is exactly what the Model Context Protocol (MCP) : an open standard that makes it easy for AI agents to connect to their environment.

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What is MCP?

The Model Context Protocol (or MCP) is an open-source protocol that allows AI agents to easily interact with various data sources: databases, APIs, file systems, code repositories, etc.
Rather than having to manually integrate each tool or source, the MCP provides a standard interface to automate this connection.

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A simple and modular architecture

The functioning of MCP is based on three main components:

• MCP host : The application in which the AI agent is integrated (for example, a chat interface or an IDE).
• MCP customer : A component responsible for communicating with MCP servers and orchestrating requests.
• MCP server : The contact point between the protocol and external tools. It connects to data and services (database, API, files...) and executes queries on demand.

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Example of operation

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When a user asks a question like: “What is the current weather forecast in Paris? ” , here are the steps that take place in the background:

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1. The host forwards the request to MCP customer.
2. The customer questions one or more MCP servers for a list of available tools (for example, a weather API).
3. This information is passed to the language model.
4. The model chooses the most suitable tool to meet the demand.
5. The client triggers a call to the MCP server concerned.
6. The server queries the data source (in this case, the weather API) and returns the results.
7. The template formats the response, which is then displayed to the user.

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Why adopt the MCP?

The MCP protocol has several concrete advantages:

• Standardization : a single interface for interacting with a wide variety of systems.
• Modularity : each server can be deployed independently, making it easy to add new features.
• Interoperability : compatible with various tools (SQL/NoSQL databases, REST or GraphQL APIs, file systems...).
• Separation of responsibilities : AI agents remain focused on their logic, while the protocol takes care of data connections.

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Possible applications

The MCP can be used in many contexts:

• Of professional chatbots able to access internal documents or CRMs.
• Of development assistants that analyze code in a Git repository and suggest fixes.
• Of search tools connected to internal databases.
• Of automation solutions intelligent systems that interact with different information systems.

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StackEasy, an accelerator to create your own AI agents

For businesses looking to take advantage of Model Context Protocol, companies like Stackeasy allow you to quickly design, configure, and deploy custom AI agents.

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Stackeasy is an agency specialized in the implementation of tailor-made artificial intelligence agents for companies. It supports its customers in the design, integration and deployment of agents connected to their business tools (CRM, ERP, databases, etc.), relying in particular on standards such as the Model Context Protocol (MCP). A turnkey solution to take advantage of AI in a concrete, secure and effective way.

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Conclusion

The Model Context Protocol provides an elegant and standardized response to a central challenge in the development of AI agents: giving them fluid and intelligent access to data and tools.

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This is not only a technical improvement, but a real paradigm shift in how AI agents interact with their environment. MCP could well become an essential base for building tomorrow's AI applications.

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