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MCP: The Next Frontier of AI Tooling for Businesses Is Here
From fluent conversations to insightful responses, AI continues to amaze us every day. But forward-looking businesses expect a tad bit more. They want AI agents that not only respond to queries but also take appropriate actions, like drafting emails, querying databases, updating spreadsheets, and even configuring workflows without human intervention. That's where MCP comes in.
As an open protocol, MCP (Model-Code-Prompt) is all about action. Introduced in late 2024, MCP is fast gaining traction to bridge AI models with a range of tools and software that businesses commonly use. This article offers a layman's understanding of what MCP is, why it matters for AI tooling with real-world examples, and the road ahead. Keep scrolling!
What is MCP (Model-Code-Prompt)?
MCP was originally referred to as Model Context Protocol. Simply put, it’s an open standard that allows AI systems to readily connect and interact with external software, data sources, and services. Thus, it acts as a universal interface for large language models, making it easy to call for external tools, execute code, and fetch live data within the prompt and response cycle.
Ask any developer, and they will tell you how tedious it is to work with custom-built integrations for different apps or APIs. But with MCP, you have a standard protocol for every tool, a lot like having plug-and-play adapters for AI. From running a database query to setting up communication parameters for Slack, you're all covered!
MCP has its roots in LSP (Language Server Protocol), which is a JSON-RPC-based protocol used in developer tooling. LSP created a standard that code editors could use to talk to programming language analyzers. It made sense, especially for incorporating features like error checking or autocompletion for different editors. But, LSP was reactive, which means it would only respond when a user typed in an IDE (Integrated Development Environment).
In contrast, MCP is mindfully designed for autonomous AI agents. The benefits are multifold. MCP is capable of planning and taking actions like never before:
They can decide and pick the best AI tools in a certain order
They can figure out different ways to accomplish a particular task
They can defer to a human for confirmation or ask for additional input when needed
Such features are vital for both medium and large-scale enterprises, especially when oversight is required for new projects. At a higher level, the architecture of MCP is divided into two parts — servers and clients. The MCP server is more of a wrapper for an external capability, like a database, email service, or a SaaS app, thereby exposing the functions of the tool in a standard format.
In comparison, the MCP client is a particular AI app connecting to the servers. For instance, an IDE plugin or a chatbot that helps broker conversations between AI models and other tool servers. Once the tool connects with an MCP server, any other AI client supporting MCP can use the same tool. Decoupling for multiple AI integrations at its best!
Imagine a language model that uses multiple tools in sequence, like calculators, stock data API, search engines, etc. to answer complex user queries. This would normally require developers to predefine new workflows for every query. But with MCP, an AI agent can dynamically decide on the best tools in a certain order without any human intervention.
Why MCP Matters for Future of AI Tooling
From creating a befitting AI tooling momentum to boosting human-machine governance, MCP is undoubtedly a game-changer for more than one reason.
<Infographic on why MCP matters for AI tooling>
Unmatched plug-and-play intelligence: What USB did for hardware by offering a standard plug, MCP is doing the same for AI. And it's taking things up a notch by replacing fragmented integrations with a universal protocol. For developers, there's no need to write custom code for each service with AI integrations. Instead, they can directly work with MCP to connect to any preferred AI tool matching a compatible server. In doing so, they can dramatically reduce the effort to bring new capabilities to an AI agent.
Transforming AI agents from passive to active: Most traditional LLM applications are either read-only or advice-giving types. With MCP, the AI agents are becoming action-takers. They can autonomously analyze what a user wants, fetch relevant data, and perform the required calculations. Such passive-to-active transition is a transformative experience for any business process.
Ushering flexible and autonomous workflows: One of MCP's significant contributions is imparting dynamic decision-making power to AI agents. Forget AI pre-programming with a fixed sequence. MCP is here to give your preferred AI a toolbox. Once the AI agent figures out the best tools based on the context, unseen scenarios can be handled with ease. Having such autonomous workflows can be a game-changer for organizations dealing with data exchange tasks.
Human-in-the-loop and governance: MCP has never been a free-for-all automation option minus oversight. It was initially designed for real-world users. That's why the protocol it uses allows approvals and checks after every critical action. For example, an AI agent using MCP can draft an email and then take a pause until human approval comes through before sending it out. This unique human-in-the-loop capability ensures control and instills trust in AI-driven actions, especially for customer-facing roles.
MCP in Action- Real World Examples
With OpenAI incorporating MCP support across their tooling SDKs in 2025, there's been a significant movement across the ecosystem. The modern marketplace looks rich with new MCP-compatible tools. Developers are now more focused in creating MCP servers with extended capabilities.
Take companies like Apollo.io and Block Inc. for instance, where they have integrated MCP into their daily workflows. Leading developer platforms like Sourcegraph and Codeium are also working extensively to improve products with MCP-driven features. But it was Anthropic's release of pre-built MCP servers for Google Drive and Slack that set the ball rolling.
The American AI startup proposed a new standard to help AI assistants seamlessly retrieve files from G-Drive and summarize Slack threads on user demand. Besides, one can use an Atlassian MCP server to integrate with Confluence for easy documentation and Jira for smart ticketing. Using a GitHub MCP server, an AI agent can easily fetch a set of code files from a known repository without full access. Additionally, it can manage multiple version control tasks like drafting commit messages and creating branches.
The Way Forward for MCP in AI Tooling
The rapid rise of MCP shows that it has successfully addressed the barriers to AI adoption. Sure, it's still early to make a comment, but the evolving protocol is downright impressive. As more best practices of working with MCP are known, AI tools will become more agentic. That means, fetching data more proactively, calling the APIs, and executing crucial tasks without human intervention.
For world businesses, MCP opens up exciting possibilities - a future where deploying AI assistants won't take years to develop. One can easily configure the best AI tools for work, just like setting permissions and immediately putting them to use. However, things are easier said than done. You will need a worthy execution partner to make it work.
At VGTS, we are a robust 360° tech company with deep passion and expertise in AI, cloud-native architecture, and next-gen digital platforms. Whether you need to assess MCP to streamline operations or build custom AI agents, our end-to-end support can help you capitalize on your investment and drive innovation and efficiency.
If this sounds like everything you could've asked for your business growth, we would love to connect. Click the link below to get going!
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