Key facts: Model Context Protocol (MCP) is emerging as the USB-C port for artificial intelligence. This open-source standard replaces complex connections with a universal language, enabling models to communicate fluidly. Supported by Anthropic, OpenAI and Google, it turns technical integration into child’s play, to power truly useful and connected AI agents.
Have you ever cringed at the current fragmentation, where connecting your data to an mcp ia systematically requires complex, bespoke development? This open-source standard has arrived to put an end to this incessant tinkering, finally offering a universal interface comparable to a USB-C port to connect any tool to your models. Together we’ll see how this ingenious protocol, now supported by the industry, mathematically simplifies your integrations and paves the way for a new generation of truly useful agents.
MCP IA: the standard that finally puts AI in order
The real problem: the Wild West of AI connections
Look at the current mess. Each tool, each database requires its own custom-built connection. We’re swimming in information silos and fragmented, never-ending integrations. It’s a colossal waste of resources for developers.
This technical complexity puts a brutal brake on the emergence of really smart, connected applications. AI remains stuck in its own bubble, unable to dialogue fluidly with the outside world.
Before MCP, connecting an AI to a new tool was like reinventing the wheel every time. A nightmare of complexity and fragmentation for developers.
The analogy that saves everything: the USB-C port of artificial intelligence
Think of the MCP IA as the USB-C port of the digital ecosystem. Just as this cable unified our chargers, this standardization protocol finally harmonizes artificial intelligence communication.
Regardless of the make of your model or the external tool connected to it, the connection becomes universal. The aim is simple: for everything to fit together and function without the slightest technical friction. That’s true interoperability.
This standardization unlocks far more powerful connected applications, something that only yesterday was impossible.
The origins of the protocol: Anthropic’s initiative
We owe this technical initiative to Anthropic, who unveiled the Model Context Protocol (MCP) in November 2024. They have laid the foundations for a necessary change.
Their vision is clear: to build an open, open-source standard that the whole community can adopt and contribute to. It’s not a proprietary technology kept under lock and key.
This collaborative approach is the only way toavoid a single player dictating the rules of AI interconnection.
Under the hood: how MCP really works
Now that we’ve grasped the “why”, let’s move on to the “how”. There’s no black magic here: the MCP is based on a formidable, well-thought-out architecture.
A client-server architecture, but for AI
Imagine a well-organized restaurant. On one side, you have the MCP clients (your AI applications like Claude or an IDE). On the other, the MCP servers who hold the ingredients (your data, your tools). The protocol is simply the standardized language they use to understand each other.
It’s a secure, two-way exchange. Your AI doesn’t just passively read data; it can execute real actions via the tools the servers expose.
There’s no need to reinvent the wheel: the system is based on the JSON-RPC 2.0 standard, transported via HTTP or stdio. It’s robust and proven.
The 3 fundamental building blocks of the protocol
To structure this communication, the MCP defines three basic concepts. These “primitives” form the heart of the system and dictate what is technically possible.
- Tools: Actions that can be executed by the AI. Think “send an e-mail”, “read a local file” or “search for information in a database”.
- Resources: The targets of these actions. A specific file, a CRM contact, a project… This is the tangible, accessible context.
- Prompts: standardized queries including instructions and context to guide the model. To master this aspect, it’s useful to understand what an AI prompt is.
The end of the M×N puzzle: radical simplification
The real problem was the “M×N” equation. To connect M AI models to N tools, you had to develop M multiplied by N unique connectors. An exponential maintenance nightmare that paralyzed developers.
MCP changes the mathematical game. Thanks to the standard, all you have to do is create M clients and N servers. The equation becomes a simple addition of “M+N”. Everything connects instantly.
This mathematical simplification drastically reduces technical complexity, making the ecosystem finally scalable and far less costly.
The MCP ecosystem in action: who’s already taken the plunge?
A standard looks good on paper. But who actually uses it? Adoption is the real test, and on this point, the MCP quickly scored points.
From pioneers to tech giants
Anthropic was obviously the first to integrate MCP into its own products, such as Claude desktop applications. It was the logical next step to get the ball rolling.
Adoption took on another dimension with the arrival of the other two giants. OpenAI officially adopted MCP in March 2025 for ChatGPT and its Agents SDK. Google DeepMind followed suit in April 2025, confirming support for its Gemini models.
When these three major players align themselves on a standard, it’s no longer an experiment, it’s an industry-wide movement.
The old world vs. the new: a hard-hitting comparison
To visualize the change, nothing beats a direct comparison chart.
| Aspect | Before MCP (M×N integrations) | With MCP (Standard M+N) |
|---|---|---|
| Complexity | Very high, exponential. Every connection is a project. | Reduced, linear. Create a reusable client or server. |
| Reusability | Weak. A connector for Slack-ChatGPT does not work for Slack-Gemini. | High. An MCP server for Slack works with any MCP client. |
| Tool discovery | Manual. The developer must explicitly code each capability. | Automatic. The MCP client can discover the tools available on a server. |
| Maintenance | Costly. Each API update can break N integrations. | Simplified. The MCP server is updated, and all customers benefit. |
Use cases that speak for themselves
Pre-built MCP servers have been shared by Anthropic for popular tools like Google Drive, Slack, and GitHub.
The impact is tangible in the field. Companies like Block and Apollo have integrated it. Developer tools such as Zed, Replit and Sourcegraph use it to help their AI agents retrieve code context more efficiently.
Adoption isn’t just theoretical, it’s already in production and solving real problems.
What does this mean in concrete terms? Possible new applications
Adoption by the big names is one thing. But for us, users and creators, what does it unlock in concrete terms? The possibilities are far more exciting than a simple technical standard.
AI assistants boosted by real-life context
Do you see the current problem? Our AIs are sorely lacking in context. They don’t know what’s in your local files, your emails or the status of your projects. MCP is a radical game-changer.
Take a concrete case. Ask your AI: “Give me a summary of project X”. It will be able to connect directly to the MCP server of your management tool, such as GitHub, to read the latest commits and comments.
AI is no longer an amnesiac black box, but a truly informed colleague. It’s one of the best AI assistants imaginable.
Towards truly autonomous AI agents
Let’s take the concept a step further. With standardized access to tools, AI can go from simple respondent to actor. This is the birth of autonomous AI agents.
With MCP, we’re no longer just talking about assistants who respond. We’re talking about agents who act, collaborate and solve complex problems on our behalf.
Imagine an agent planning a trip by connecting to the MCP servers of an airline, a booking site and your calendar. The potential of AI agents is exploding.
AI collaboration (A2A): the next big thing
Here’s an often overlooked blind spot: Agent-to-Agent (A2A) communication. MCP isn’t just about connecting an AI to a tool.
It can also be used to connect AIs. For example, an agent specialized in data analysis could “chat” with an agent specialized in report writing.
It’s a vision of an ecosystem of collaborative AIs, each with its own skills, united by a common language: the MCP.
All is not rosy: the challenges and points of vigilance of the MCP
The current enthusiasm is palpable, but we need to keep a cool head in the face of technical reality. Like any powerful new technology, PCM inevitably brings its share of thorny questions and potential risks that can’t be ignored.
Security vulnerabilities not to be taken lightly
Massive adoption hides a worrying technical reality that deserves your immediate attention. As early as April 2025, security researchers were sounding the alarm about critical vulnerabilities identified shortly after the giants announced adoption. These vulnerabilities expose corporate infrastructures to real threats.
- Prompt injection: A malicious user could manipulate the AI to use connected tools in an unauthorized way, bypassing the usual safeguards.
- Permission issues: A poorly configured tool could allow the AI to access more data than expected, often leading to sensitive files being exfiltrated to the outside world.
- Tool spoofing: The major risk of malware masquerading as a trusted tool in order to deceive the AI and the end-user.
The risk of fragmentation: a standard for governing them all?
Is MCP really THE universal standard, or just the first of many to come? The history of tech is full of “standards wars” that often end badly. You think you’ve got the miracle solution, then the reality of the market takes over. It’s a classic cycle.
The real risk is that other major players will offer their own competing protocols, recreating the very fragmentation that the MCP seeks to eliminate.
The official adoption by OpenAI and Google is an excellent signal, but the ecosystem is still young and the game is not yet up.
Governance and control: who steers the ship?
Let’s address the central question of project governance. The protocol may be open-source, but who really decides on its future evolution? Who validates technical changes?
The establishment of a clear and transparent governance structure will be decisive for its longevity. We need a neutral foundation, like the Linux Foundation, to reassure everyone. Without this, business confidence will remain fragile.
The obvious risk is that Anthropic or another major player will end up exerting an inordinate influence on its long-term development.
The future of MCP: a simple protocol or the foundation of tomorrow’s AI?
Despite these challenges, MCP’s trajectory is impressive. So, where do we go from here? And what’s really at stake behind this standard?
Open-source as a driver of innovation
MCP’s real tour de force lies in its open-source architecture, accessible to all. Any developer can now build a server or a client without having to ask permission.
This total freedom is encouraging a surprising explosion of bottom-up innovation. Independent developers are already creating servers for niche tools, enriching the ecosystem at breakneck speed without waiting for approval from the Silicon Valley giants.
Such a collaborative approach makes the ecosystem resilient, able to scale much faster than traditional proprietary ChatGPT alternatives.
Next steps for the ecosystem
Now let’s look to the near future: what are the top priorities if the MCP is to live up to its ambitious promises and become a must-have?
- Enriching SDKs: The aim is to provide even simpler and more powerful development kits in more languages, such as Kotlin or Swift, to attract all coders.
- Standardization of security: It is essential to develop robust security standards (permissions management, authentication via OAuth) integrated into the protocol to reassure reluctant companies.
- Creation of a tool registry: The creation of a public and reliable directory of MCP servers is planned to facilitate their discovery and widespread use by the community.
My opinion: why it’s more than a technical standard
Let me be frank: the MCP is not just a technical detail for back-office developers. It’s a key component in building a more open, transparent and useful AI, far removed from the closed ecosystems that often restrict us.
This standard is the essential bridge between the abstract potential of AI models and concrete real-world applications. In my view, it is without doubt one of the most important building blocks for the future of our interaction with AI.
MCP is not just another technical acronym. It’s the missing link that transforms our chatty AIs into
FAQ
In concrete terms, what is the Model Context Protocol (MCP)?
Imagine a kind of “universal plug” for artificial intelligence. The Model Context Protocol (MCP) is an open-source standard, launched by Anthropic in November 2024, that makes it easy to connect AI assistants (like Claude or ChatGPT) to your external data and tools (files, servers, applications). Before it, it was chaos: you had to create a specific connector for each tool. Today, the MCP acts as a common language that enables AI to “understand” and interact with your digital environment without complex tinkering.
What does the acronym MCP stand for and what is its precise role?
MCP stands for Model Context Protocol. Its role is in the name: to provide “context” for the “model”. Instead of having an isolated AI that knows nothing about your current projects, MCP creates a secure bridge (via a client-server architecture) so that it can read documents, query a database or execute commands via standardized tools. This is what turns a simple chatbot into a real assistant capable of acting on your real systems, all via a unified protocol (often based on JSON-RPC).
Why are we talking about MCP as the new AI standard?
MCP is called a standard because it solves the problem of fragmentation. Rather than closed, proprietary integrations, MCP is an open, interoperable standard. It’s a bit like the USB-C port for artificial intelligence: it doesn’t matter what AI model (client) or data source (server), as long as they speak “MCP”, they can connect. Rapid adoption by giants like OpenAI and Google DeepMind in 2025 confirms that this is the foundation on which the future of AI agents will be built.
