For safe and scalable AI integration For safe and scalable AI integration For safe and scalable AI integration Introduction When agent mode was released in different AI tools in early 2025, it gave me a strange sense of déjà vu. It was similar to early days of web APIs, when there was no standard way to expose API, every product had its own way of handling authentication, payloads, and errors. Before REST came, it was a mess. You have to keep rewriting the same logic again and again for each API you want to integrate with. Rest provided a lightweight system that was flexible, scalable, reusable, and intuitive. Trying to use APIs inside ChatGPT or other AI tools reminds me of those days of non-standard integration, where you feed in the API specs, share credentials, and hope it works. However, everything you have done has to be repeated by every team member; not only that, every team member has to make sure that they are on the same version of the API and provide the correct specification, otherwise the outcome of the AI agent will be drastically different. On top of that, there’s no easy way to see what APIs exist or what they can do. Security teams flagged it as a risk due to credentials floating around, unclear scopes, and no audit trail. It was clear to me that there was a need for a cleaner way that AI tools can talk to our systems. The Problem Before MCP When I first started using AI tools like GitHub Copilot for day-to-day work, I wanted to connect them to internal systems to do something useful like checking the status of rally stories or getting information from GitHub, or reading logs. That’s when the limitations of connecting to various APIs from AI agents became clear. Each tool defines the APIs in its own way. You had to share tokens, configure URLs, and provide API specifications and documentation to the AI agent. Everyone on the team had to go through all the same steps to set up the APIs so that they could be accessed by the AI agent. It was not scalable and started to become cumbersome to a level that not everyone will configure all the tools, which limits the overall benefits of the AI agents. Another hurdle was that it was unknown how the AI agents used the data provided; hence, every time the API access was needed, it had to be approved by security for each team member. Approvals are delayed without a standard model. It reminded me of the early API days again. Everyone wanted to connect systems, but plumbing was not standardized. How MCP Changes That MCP gained prominence with Anthropic, making it open source. It clearly stands out in the way it organizes the interaction between AI and real systems. You don’t have to change the systems you already have, as MCP is built on top of your existing systems. MCP simply standardizes the discovery of these systems so the AI clients can interact with them. That first marvelously like a piece in a jigsaw puzzle. The core idea of MCP is the separation of responsibility. An MCP Server describes what your system can do by exposing the entry point known as Tools. An AI Client brings the reasoning ability, and it does not have to know the intricacies of how to call your API. Client interacts with Server and utilizes the tool to send a request to perform any tasks like invoking an internal service, reading the contents of a file, performing a search, or running a workflow. To the AI, it is just a simple prompt that translates to “call this tool with these inputs.” MCP also includes Resources and Prompts. Resources are the documents, logs, or configurations stored somewhere that AI can utilize. Prompts are reusable instructions. They help the AI to utilize the resource in a consistent way. These are helpful so that you can reuse the prompts, or even go further and try to standardize the prompts so that various team members across the enterprise can utilize them. They also help us to generate the uniform output for use cases like documenting, creating user stories, following coding standards, or making sure it follows all security guardrails when generating code. Once defined, all these work hand in hand as you have now created a shared map of what your systems can do in the way you want. The AI client can discover the available tools without feeding in the specs each time. Everyone on the team utilizing the same configuration will see the same capabilities, described the same way. It reduces the duplication of work, causes less errors, and provides predictable outcomes. What is commendable here is that MCP did not require you to reinvent your architecture. It allows you to keep your APIs, your security controls, and your environments as they are. MCP just sits on top of existing systems, with understanding of those systems coming from resources and prompts. It’s like how an ATM works with a bank. ATM exposes a set of actions you can safely perform. MCP does the same for AI systems. MCP will expose only certain actions as defined in resources, and it will perform those actions in a consistent way as defined in prompts without knowing the internal systems. Why It Matters As we start utilizing AI tools and connect to real production systems, we increase the chances of AI tools having access to sensitive data. If not handled carefully, sensitive data can get exposed in the logs or prompts. A small action can have unintended side effects; without proper guardrails, it can be disastrous. The Samsung incident is a good example of how easy it is to expose something unintentionally. I have seen many similar situations on a smaller scale in day-to-day work. How does MCP help? You define exactly what actions can be performed for AI.  Which systems are visible? Which are read-only? And which operations require manual confirmation? All this is defined in the MCP tool configuration. The same structure applies irrespective of the system MCP is trying to expose, providing a consistent setup that is easy to review and easy to trust. A practical benefit of that is for teams; instead of each developer figuring out the use of the connection in their own way, the integration is defined once and reused. Everyone uses the same interface. The AI sees the same capabilities leading to fewer surprises and more predictable results, which is something engineering teams value a lot. It is similar to how we interact with an ATM that gets the work done safely, through a controlled interface. You get what you need without seeing or exposing everything happening in the bank system behind it. How It Fits into the Bigger Picture MCP felt like the right idea at the right moment when it arrived in late 2024. There was considerable time teams had to spend trying to enable safe agent actions, making sure sensitive data was not exposed. Still, everyone was solving the problem in their own way. MCP provides a structured and reliable way to do this while saving a lot of effort. The timing of MCP was right as agent mode was appearing on several platforms. Everyone in the software development field was experimenting with AI to improve the real-world workflows. Because of the structure MCP provided, a lot of ad hoc AI integration turned into a predictable interface. MCP adoption is growing steadily, with developer tools like Claude Desktop and Cursor supporting MCP as one of the primary modes of AI interaction with files, terminals, and external APIs in a controlled way. Since then, the open-source community has released many open-source MCP servers for common workflows, and new ones are being released very frequently. MCP is being used by many enterprises to expose internal DevOps and NetOps workflows without bypassing security controls. In short, the MCP has gained a lot of momentum since its release. MCP has given us a foundation. It will be exciting to see what kind of workflows are going to emerge from its use. Where It Goes Next With the growth of the MCP ecosystem, a lot of patterns are going to emerge. With wider adoption in production-ready systems, it will make it easy for AI agents to connect to real workflows. The future looks like where focus will be on what capabilities should be exposed and what kind of governance is needed around them, instead of figuring out how the capability can be exposed. It feels like MCP is going to be a standard for agentic AI communication with real systems. The beginning of MCP is promising. It is refreshing to see a model that provides a consistent, secure, and reusable way of building AI integrations.

This story contains new, firsthand information uncovered by the writer.

The is an opinion piece based on the author’s POV and does not necessarily reflect the views of HackerNoon.

This story contains AI-generated text. The author has used AI either for research, to generate outlines, or write the text itself. 

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