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Integrating artificial intelligence into company information systems is becoming increasingly essential for automating tasks and improving efficiency. Until recently, connecting an AI model (such as an LLM, Large Language Model) to business applications required complex development. With the emergence of the Model Context Protocol (MCP), this has become far simpler and faster.
What is the Model Context Protocol (MCP)?
MCP is an interconnection standard developed by Anthropic to let AI models access databases, business tools and online services directly.
Think of it as a universal bridge that lets an AI talk to existing systems without needing bespoke development for every case.
How does MCP work?
MCP rests on a three-part architecture:
- The host: the user interface, such as an AI application (Claude Desktop, for example).
- The MCP client: a module built into the host, responsible for passing the AI's requests to the business tools.
- The MCP server: a small program that connects to databases or services (Salesforce, Gmail, GitHub and so on) and answers requests.
A concrete example
Imagine an AI chatbot used for customer support:
- The user asks the AI to check the status of an order.
- The AI uses MCP to connect to your CRM (Salesforce, for example) and query the database.
- It returns the answer to the user in real time, without human involvement.
Why is MCP a major step forward for businesses?
Before MCP, every AI integration project required either:
- developing a specific API,
- or carrying out heavy configurations that were sometimes hard to evolve.
With MCP, everything is standardised:
- Less development: the AI uses a common language to reach the tools.
- Better security: each action can be subject to human validation.
- Scalability: hundreds of open-source MCP servers already exist for popular tools.
The benefits for your organisation
- Saving time on AI projects.
- Reducing development costs.
- Securing data exchanges with the AI.
How do you install and use an MCP server?
- Choose your MCP server according to the tool you want to connect (a Salesforce or Gmail server, for example).
- Install the server: generally, cloning the code from GitHub and following a few simple steps is enough (installing dependencies, basic configuration).
- Configure your AI host (Claude Desktop, OpenAI and so on) so that it can talk to your MCP server.
⚡ With this system, even a small technical team can connect an AI to several business tools in a matter of hours.
MCP: a protocol on its way to becoming a standard
Major companies such as OpenAI already build MCP into their systems. This confirms that the open protocol could soon become the norm for connecting AI to enterprise systems.
By combining MCP's strengths with custom AI solutions built on technologies such as RAG, LangChain or open-source LLMs, it becomes possible to build genuinely intelligent, adaptable and secure digital tools.
FAQ: understanding MCP and its business applications
What is an LLM?
An LLM (Large Language Model) is an artificial intelligence model specialising in understanding and generating natural language text. Examples: ChatGPT, Claude, Gemini.
Is MCP compatible with every AI model?
MCP was designed to be universal. Any AI model can potentially use it, provided it has a compatible host.
Is MCP secure?
Yes. You can configure manual checks for every action the AI takes through MCP, limiting the risk of unauthorised access.
Can you use MCP without being a developer?
Some IT grounding is advisable to install an MCP server, but plenty of open-source resources make the process relatively accessible.
Which tools are already MCP-compatible?
Among the hundreds of open-source MCP servers available: Salesforce, Gmail, Notion, GitHub, Stripe and more.
Want to integrate innovative AI solutions into your business tools?
Gensai develops custom digital solutions connected to your environment through the latest technologies such as MCP, RAG and LangChain, while respecting standards of ethics, security and responsibility (GDPR, frugal AI).