The Model Context Protocol (MCP) represents a significant advancement in AI integration, providing a standardised way for AI models to connect with external data sources and tools. Understanding MCP is essential for organisations that want to maximise the potential of their AI system. How? By enabling access to the information and tools that the AI system needs to deliver optimal results.
This article explains what MCP is, how it functions, its specific applications for AI agents, and its compatibility with AI platforms, such as ChatGPT.
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What is MCP and How Does it Work?
Defining Model Context Protocol
The Model Context Protocol (MCP) is a new system that enables AI tools, such as ChatGPT, to connect easily and securely with other tools, data, or software. Anthropic introduced it in November 2024 to address a common problem: providing AI with access to the correct information without requiring complex setups.
You can think of MCP as a USB-C port for AI. Just like a USB-C port allows you to plug various devices into your computer, such as a phone, a keyboard, or a monitor, MCP enables AI systems to connect to different types of data and tools using a single standard method. This means AI developers don’t have to build custom connections every time. It saves time, reduces errors, and makes AI more useful in different real-world situations.
The Problem MCP Solves
Before MCP, connecting AI models to various data sources was a very time-consuming process. Every time an organisation wanted to link an AI tool to a new data system, they had to build a separate custom connection for it. Organisations faced what Anthropic described as an ‘N×M problem‘ – with ‘N’ representing different large language models (LLMs) and ‘M’ representing different tools or data sources.
For example, if a business uses 3 AI models and has 5 different internal systems, it would need to build 15 different connections. Each one required time, money, and careful setup to maintain security. And if they added a new AI tool or a new system? That meant even more work.
This made it hard for companies to use AI effectively. Even the most advanced AI models cannot function effectively if they lack easy access to the necessary data. MCP addresses this by establishing a single, standardised way to connect everything.
How MCP Works
To make it simple, MCP works like a bridge that helps AI tools talk to other software and data sources in a more organised way. Here is how it works:
- MCP Clients are added to AI tools (like ChatGPT or Claude). They help the AI request and receive the necessary information.
- MCP Servers are small programs that connect to documents, databases, or business apps. They know how to answer the AI’s questions by finding and sharing the correct data.
Data Flow Process:
- The AI application (via MCP client) sends a request for information.
- The MCP server receives the request and searches for that information.
- It organises the data into a format the AI can understand.
- The data is sent back to the AI tool, which then uses it to provide more accurate answers.
Practical Example of MCP in Action
To illustrate how MCP works in practice, consider this scenario:
A business analyst is using an AI assistant to analyse quarterly sales data. When the analyst asks, “How did our Q2 sales compare to projections?” the following occurs:
- The AI assistant (via its MCP client) recognises it needs access to both sales data and projection data.
- It connects to the appropriate MCP servers (one for the CRM system containing actual sales data and another for the financial planning system with projections).
- Each MCP server retrieves the relevant data from its respective system.
- The data is returned to the AI assistant in a standardised format.
- The AI model utilises this contextual information to generate an accurate, data-driven response that compares actual sales to projections.
Without MCP, this interaction would require custom integrations between the AI assistant and each data system or manual data retrieval by the analyst.
Can ChatGPT Use MCP?
OpenAI’s Adoption of the Model Context Protocol
Yes, ChatGPT can use MCP. In March 2025, OpenAI officially adopted the Model Context Protocol, integrating it across its product ecosystem. This adoption represents a significant endorsement of MCP as the industry standard for AI data connectivity.
OpenAI CEO Sam Altman announced that MCP support would be available through multiple channels:
- OpenAI Agents SDK: Developers can build ChatGPT-powered applications that connect to MCP servers.
- ChatGPT Desktop Application: The desktop version of ChatGPT enables users to connect to local MCP servers, providing ChatGPT with secure access to files and applications on their computer.
- Responses API: OpenAI’s API services are being updated to support MCP connections, enabling developers to build applications where ChatGPT can access external data sources through the standardised protocol.
The Future of MCP
Growing Ecosystem and Adoption
The MCP ecosystem is expanding rapidly, with over 5,000 active MCP servers listed in public directories as of May 2025. Major AI providers, including Anthropic, OpenAI, and Google DeepMind, have all adopted the protocol, suggesting it may become the de facto standard for AI-data connectivity.
This growing adoption creates network effects that make MCP increasingly valuable. Each new server implementation expands the capabilities available to all MCP-compatible AI systems.
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Potential Advancement
As MCP technology continues to grow, we might see improvements like:
- Better Standardisation – Making the system work even more smoothly in tricky or unique situations.
- Stronger Security – Adding more innovative ways to keep data safe and protected.
- Faster Performance – Enhancing the speed at which data is shared between systems.
- More Features – Letting AI do more than get data, like using tools or taking actions automatically.
Implications for the Future of AI
MCP is a crucial step toward making AI systems more innovative and valuable. Instead of getting stuck trying to find the correct information, AI can focus on doing what it does best: thinking, understanding, and giving helpful responses. Because MCP makes it easier for AI to integrate with existing tools and data, more businesses will be able to utilise AI without requiring complex setups.
It’s a new way for AI to obtain the information it needs quickly, securely, and in a standardised manner. This helps AI tools like ChatGPT become more aware of the context in which they’re working so they can provide better answers.
As more companies begin to use AI, understanding MCP will be crucial in ensuring those tools function effectively and meet your business needs. It’s not just about making AI smarter, but it’s about making it more connected and valuable in the real world.
About the Author
Putri is a Digital Marketing Specialist at Upscalix. With 7 years of experience in digital marketing, she specialises in SEO, social media campaigns, and performance analytics, helping businesses reach their target audiences effectively.
At Upscalix, Putri is crucial in crafting and executing marketing strategies highlighting the company’s innovative offshore development solutions. Her ability to blend creativity with analytical thinking ensures that every campaign delivers measurable results. Contact her at LinkedIn.
