All about AI agents and MCP protocol: what about for your business

Jun 24, 2025 | Ai agents

Introduction

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, performing complex tasks in real time. At the heart of this revolution is the MCP (Model Context Protocol), an open-source protocol that serves as a universal connector between large language models (LLMs) and an organization’s application ecosystem.

In this article, we’ll explore in depth why MCP is a game-changer for building enterprise-scale AI workflows and agents. We’ll see how this protocol standardizes access to external resources, enhances scalability, and enables unprecedented agility when selecting AI models. We’ll conclude with practical demos, security best practices, and future outlooks.

I. The limits of traditional approaches

1. Classic API architecture

Historically, when an application wanted to offer AI capabilities, it relied on individual API calls for each external service. For example, a chatbot that generated text had to be paired with scripts, microservices, or custom connectors to link to CRMs like Salesforce, internal databases, or analytics tools such as Google Analytics.

  • Writing dedicated documentation for each target API.
  • Developing a custom translation module between the API’s format and the LLM’s request format.
  • Ongoing maintenance, especially with every API or AI model update.

Once the number of tools to integrate became significant (often more than ten), complexity skyrocketed, making the solution hard to maintain and expensive to evolve.

2. Scalability and agility challenges

  • Technological rigidity: Each new tool or update required substantial rewrites or adjustments of connectors.
  • Tight coupling: The chatbot and its connectors were inseparable, limiting the ability to swap out the underlying AI model.
  • Skill fragmentation: Development teams needed expertise in every external API plus LLM prompt-engineering nuances.

In short, the traditional approach resembled a patchwork of non-standardized parts, where each addition demanded a full engineering project.

MCP integration illustration

II. MCP: a universal connector for AI

1. Definition and core principles

The Model Context Protocol (MCP) is an open-source protocol defining a standard interface to supply context and capabilities to large language models. Instead of creating a unique module for each service, you deploy one or more MCP servers—true connectors—that expose uniform functions for an MCP client (the LLM) to consume.

  1. Standardization: All servers implement the same REST API specs, 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 supporting the protocol.
  4. Security: Permissions and access are centrally managed and granular.

2. The role of the “connector”

You can liken an MCP server to a universal power adapter: no matter the plug shape of the tool (Salesforce, Google Drive, SQL database…), you plug in the same MCP cable, and the AI client powers or queries the resource as needed. This image better captures the protocol’s simplicity and uniformity compared to a translator metaphor.

  • Plug-and-play: Installing an MCP connector is like plugging in a single adapter without rewiring.
  • Reversibility: You can unplug and replace a connector without disrupting the system.
  • Modularity: Each connector is managed independently and can be added or removed at will.

III. Technical architecture of MCP

1. Main components

The MCP protocol revolves around two entities:

  • MCP server: exposes a uniform endpoint (/mcp/v1/*), handles authentication, permissions, and translates to the target service’s native API.
  • MCP client: integrated into the AI application, it orchestrates MCP calls, discovers capabilities, and translates responses back to the LLM.

End-to-end flow

  1. The MCP client sends a POST /mcp/v1/perform request with a standard payload.
  2. The MCP server validates the token, checks permissions, and invokes the native API (e.g., Salesforce).
  3. The response is returned in a standardized format.
  4. The client integrates this context into the prompt sent to the LLM.

2. Deployment modes

  • Cloud servers: hosted and maintained by service providers or third parties—ideal for rapid deployment.
  • On-premise servers: deployed within your own infrastructure for maximum control and data privacy.

IV. MCP server catalog

The variety of MCP servers offers options to meet diverse business needs, whether official, community-built, or fully custom solutions.

1. Official integrations

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

These official servers deliver high reliability and enterprise-grade security.

2. Community servers

Community contributors maintain connectors for tools like Airtable, Asana, and Slack. They fill gaps but require careful security review.

3. Custom servers

Using Python or JavaScript SDKs—or no-code platforms like n8n—you can build bespoke connectors for your internal systems.

V. MCP clients: onboarding and features

MCP clients are the AI applications end users interact with, exposing connector capabilities in a seamless interface.

1. ChatGPT

  • MCP support via “deep research” mode for reading external data (emails, CRM records).
  • Currently read-only—creation or modification of external objects is not yet supported.
  • Ideal for reports, summaries, and data analysis.

2. Claude

  • More comprehensive MCP support, including creation and modification of external resources.
  • Enables direct execution of business workflows and tasks.

3. Gemini and other LLMs

  • Integration through SDKs and APIs, still maturing.
  • MCP standardization allows easy onboarding of new models without rewriting connectors.

VI. Practical demonstrations

The following demos show how to activate, configure, and use MCP connectors with various tools, from ChatGPT to n8n, enabling rapid deployment of operational AI workflows.

1. Native activation in ChatGPT

  1. Go to Settings > Connectors.
  2. Select the Gmail or Calendar connector.
  3. Enable “deep research” mode.
  4. Prompt: “Retrieve all AI-related newsletters from the last six months and summarize them.”

2. Installing a Notion server for Claude Desktop

  1. Clone the official GitHub repo.
  2. Generate a Notion integration token.
  3. Paste credentials into Claude’s config file.
  4. Restart Claude and verify the Notion connector appears.

3. Building an n8n connector for Google Analytics

  1. Create a new workflow in n8n with an MCP trigger.
  2. Add the Google Analytics node.
  3. Configure service-account credentials and select metrics.
  4. Activate the workflow and copy the MCP server URL.
  5. Add this URL as a custom connector in Claude Web.

VII. Best practices and recommendations

To ensure robustness and security of your MCP agents, follow these key guidelines:

  1. Least privilege: grant only the permissions strictly needed for each connector.
  2. Modularity: limit to five connectors per server to avoid complexity overload.
  3. Explicit prompts: always specify which connector to use in your prompt.
  4. Monitoring: track usage (quotas, logs) and handle errors proactively.

VIII. Advanced use cases

  • Employee onboarding: An MCP-enabled agent integrates with your LMS and HR system to send personalized training schedules, provision tool access, and answer administrative questions in real time, streamlining new-hire ramp-up.
  • Customer support: Direct access to your internal knowledge base and CRM allows the agent to fetch FAQs, documentation, and user history to deliver ultra-personalized responses and speed up ticket resolution.
  • Urban logistics twin: Researchers connect simulation tools (AnyLogic), optimization engines (Gurobi), and reporting dashboards through MCP, iterating quickly on sustainable transport strategies without rewriting integrations.

IX. Perspectives and future developments

The MCP protocol is rapidly expanding and is poised to become the backbone of enterprise AI architectures, rendering ad hoc integrations obsolete. As new, more powerful LLMs emerge, they will benefit from this standardized connector ecosystem to deploy AI agents at scale.

Upcoming milestones include:

  • Wider adoption among SaaS vendors.
  • Stronger security and compliance standards.
  • Dedicated MCP connector marketplaces.

Conclusion

MCP, as a universal connector, transcends the limitations of classic AI architectures. It provides an elegant, modular, and secure solution to empower AI agents with extended capabilities without sacrificing flexibility or scalability. By adopting MCP, organizations can fully leverage rapid advances in language models while simplifying workflow maintenance and evolution.

We invite you to explore official, community, or custom MCP servers today to transform your chatbots into intelligent agents capable of seamlessly automating business processes.