AI Agents and MCP Protocol: New Opportunities for Your Business

June 24, 2025 | AI agents

In the age of artificial intelligence, chatbots have evolved far beyond simple conversation assistants. Modern AI agents can interact directly with a multitude of external tools and services, accomplishing complex tasks in real time. At the heart of this revolution lies MCP (Model Context Protocol), an open-source protocol that acts as a universal connector between large language models (LLMs) and enterprise application ecosystems.

In this article, we'll explore in depth why MCP is a game-changer for building workflows and AI agents at industrial scale. We'll see how this protocol standardizes access to external resources, facilitates scalability, and enables unprecedented agility in AI model selection. We'll conclude with practical demonstrations, security best practices, and future perspectives.

I. Limitations of Traditional Approaches

1. Classic API Architecture

Historically, when an application wanted to offer AI features, it relied on individual API calls for each external service. A text-generating chatbot, for example, had to be coupled with a set of scripts, microservices, or homemade connectors to connect to CRMs like Salesforce, internal databases, or analytical tools like Google Analytics.

• Writing dedicated documentation for the target API.
• Developing a specific translation module between API formats and LLM request format.
• Continuous maintenance, especially with each API or AI model update.

When the number of tools to integrate became significant (often more than 10 systems), the complexity curve skyrocketed, making the whole system difficult to maintain and costly to evolve.

2. Scalability and Agility Problems

• Technological rigidity: Each new tool or update of an existing one required significant rewriting or adjustment of connectors.
• Tight coupling: The chatbot and its connectors formed an inseparable pair, limiting the ability to replace the underlying AI model (ChatGPT vs Claude vs Gemini, etc.).
• Skills fragmentation: Development teams had to master both the specificities of each external API and the subtleties of prompts and model fine-tuning.

In short, the traditional approach resembled an assembly of disparate, non-standardized parts, where each addition required a full engineering project.

II. MCP: A Universal Connector for AI

1. Definition and Fundamental Principles

Model Context Protocol (MCP) is an open-source protocol that defines a standard interface for providing context and capabilities to large language models. Instead of creating a unique module for each service, we install one or more MCP servers, true connectors, that expose standardized functionalities that an MCP client (the LLM) can consume.

1. Standardization: all servers implement the same REST API specifications, regardless of the target service. 2. Extensibility: new connectors can be added without modifying the client. 3. Model-agnosticism: the same MCP client works with any model, as long as it understands the protocol. 4. Security: permissions and access are managed centrally and granularly.

2. The Role of the "Connector"

An MCP server can be compared to a universal electrical connector: regardless of the tool's plug shape (Salesforce, Google Drive, SQL database…), you plug in the same MCP cable, and the AI client powers or requests the resource according to your needs. This image better illustrates the simplicity and uniformity of the protocol, as opposed to the idea of a translator that needs to learn multiple languages.

• Plug-and-play: installing an MCP connector is like plugging in a unique adapter, without changing the internal wiring.
• Reversibility: a connector can be unplugged and replaced by another without interrupting overall operation.
• Modularity: each connector is managed independently, and can be added or removed at will.

III. MCP Technical Architecture

1. Main Components

The MCP protocol revolves around two entities:

• MCP Server: exposes a uniform endpoint, manages authentication, permissions, and translation to the target service.
• MCP Client: integrated into the AI application, it orchestrates MCP calls, manages capability discovery, and translates responses for the LLM.

#### End-to-End Flow

1. The MCP client sends a request with a standard payload. 2. The MCP server authenticates the token, verifies rights, and calls the service's native API (e.g., Salesforce). 3. The response is sent back to the client in normalized form. 4. The client integrates this context into the prompt sent to the LLM.

2. Deployment Modes

• Remote servers (cloud): provided by the service publisher or third parties, they are hosted and automatically updated. Ideal for rapid deployment.
• Local servers (on-premise): deployed in your infrastructure, they guarantee maximum confidentiality. More demanding in terms of configuration.

IV. MCP Server Catalog

The diversity of MCP servers offers a range of options to meet varied enterprise needs, whether official, community, or fully custom solutions.

1. Official Integrations

• Notion
• HubSpot
• Google Workspace (Gmail, Drive, Calendar)

These official servers offer increased reliability and security.

2. Community Servers

External developers maintain connectors for tools like Airtable, Asana, Slack. They're valuable for filling gaps but require security review.

3. Custom Servers

Thanks to Python or JavaScript SDKs, or via no-code platforms like n8n, you can create your own connectors for your internal systems.

V. MCP Clients: Hands-On and Specifics

MCP clients embody the visible face of this architecture, offering end users direct and simplified access to connectors.

1. ChatGPT

• MCP support as part of "Deep Research" mode.
• Allows reading external data (emails, CRM…) but not yet writing or modifying.
• Ideal for reports, summaries, or analysis.

2. Claude

• More complete MCP support, including creation and modification of external objects.
• Direct execution of workflows and business tasks.

3. Gemini & Other LLMs

• Integration via SDK and API, still maturing.
• MCP standardization facilitates their future adoption without connector redesign.

VI. Practical Demonstrations

The following demonstrations illustrate step-by-step how to activate, configure, and use MCP connectors with different tools, from ChatGPT to n8n, to quickly deploy operational AI workflows.

1. Native Activation in ChatGPT

1. Go to Settings > Connectors 2. Select Gmail or Calendar connector 3. Activate "Deep Research" mode 4. Formulate a prompt like: "Retrieve all AI newsletters received in the last 6 months and summarize them."

2. Installing a Notion Server for Claude Desktop

1. Clone the official repository on GitHub 2. Generate a Notion integration token 3. Copy the information into Claude's configuration file 4. Restart the application and verify the Notion connector appears

3. Creating a Connector on n8n for Google Analytics

1. In n8n, create a new workflow with an MCP trigger 2. Add the Google Analytics node 3. Configure access (Service Account, metrics and dimensions) 4. Activate the workflow and copy the server URL 5. Add this URL as a custom connector in Claude Web

VII. Best Practices and Recommendations

To ensure the robustness and security of your MCP agents, it's essential to follow certain key recommendations from the design phase.

1. Principle of least privilege: grant only strictly necessary rights. 2. Modularity: limit to 5 connectors per server to avoid confusion. 3. Explicit prompt: clearly indicate in the prompt which connector to use. 4. Monitoring: track connector usage (quotas, logs) and manage errors.

VIII. Advanced Use Cases

Onboarding and Training

An AI agent integrated with your LMS and HR service via MCP can automatically send training schedules, create access accounts, and answer practical questions in real time. Result: smooth integration process, reduced HR workload, and accelerated onboarding.

Customer Support

Thanks to the MCP protocol, AI agents can tap directly into the internal knowledge base to provide ultra-personalized responses in real time. This integration drastically reduces resolution time: 95% increase in customer satisfaction and 90% reduction in response time.

SafeMate: Multimodal Agent for Emergencies

SafeMate is an emergency decision support assistant that relies on MCP to orchestrate multiple services: documentary research, checklist generation, and structured synthesis. It formulates a voiced summary of the procedure to follow to guide the user in real time.

IX. Perspectives and Evolutions

The MCP protocol is in full expansion. Eventually, it could become the backbone of enterprise AI architectures, making ad hoc integrations obsolete. The arrival of new, ever more powerful LLM models will benefit from this standardization to quickly deploy AI agents globally.

Next steps include:

• Broader adoption by SaaS publishers.
• Strengthening security and compliance standards.
• Emergence of marketplaces dedicated to MCP servers.

Conclusion

MCP, as a universal connector, transcends the limitations of classic AI architectures. It offers an elegant, modular, and secure solution to endow your AI agents with extended capabilities, without sacrificing flexibility or scalability. By adopting this protocol, you prepare your business to fully leverage the rapid progress of language models, while simplifying the maintenance and evolution of your workflows.

We invite you to explore official, community MCP servers today or develop your own connectors, to transform your chatbots into true intelligent agents capable of automating your business processes in a smooth and scalable way.

Want to know more? Contact your Versatik agent.