The universal connector: how MCP lets any agent master any system

The world of artificial intelligence is moving at lightning speed. We've seen models get smarter, faster, and more capable than ever before, but a critical challenge remained: how do we connect them to the real world?

How do we give an AI agent the ability to not only understand a request but also to act on it, pull data from a customer relationship management (CRM) system, search a knowledge base, or even control an external application?

The answer came with the rise of the Model Context Protocol (MCP).

The problem: fragmented integrations

Before MCP, integrating an AI agent with external tools was a fully custom process each vendor had to come up with for every family of tools. Each tool or data source required its own unique API integration, creating a complex web of connectors that was difficult for the agent to manage, scale, and secure.

This ‘one-off’ approach limited the true potential of these autonomous entities, confining their abilities to the narrow set of tools they were explicitly hard coded to use.

It was a world of silos, where an agent's intelligence was trapped within its training data and a few bespoke connections.

The solution: interoperable integrations

MCP was introduced by Anthropic in late 2024 and has been getting traction since early 2025 with all major LLM providers. Think of it as a universal language that allows an AI agent to discover, connect to, and use any external tool or data source.

Just as a USB-C port provides a standardized way to connect a laptop to various peripherals—from monitors to external hard drives—MCP provides a standardized way for an agent to connect to a vast ecosystem of tools.

MCP enables a new architecture where an AI agent (the client) can send requests to different MCP servers, with each server acting as a translator for a specific tool. For example, a file system MCP server could grant an agent secure access to local files, while a GitHub MCP server could allow it to list open pull requests.

This standardized communication means that developers don't have to rebuild integrations for every new tool; they simply need to ensure the tool has an MCP server, and the agent can interact with it.

How AI and automation benefit from MCP

For all enterprise automation programs, the implications of MCP for AI agents are profound. Here are three key benefits:

• Agentic automation: MCP empowers AI agents to become truly autonomous. By providing a single, consistent way for the agent to access tools, MCP allows it to perform complex, multi-step workflows across different systems. For instance, an agent could check a customer's order status in one system, draft an email in another, and update a support ticket in a third, all without human intervention.

• Increased context and accuracy: the "context" in MCP is crucial. It allows an agent to go beyond its static training data and incorporate real-time, external information. This leads to more accurate and relevant responses. For example, an AI agent could generate a sales report by pulling the latest figures directly from a database via an MCP server.

• Scalability and security: for enterprises, MCP offers a scalable and secure way to deploy agents. It standardizes how an AI agent accesses data, making it easier to implement governance, monitoring, and security protocols. Instead of managing a patchwork of insecure integrations, businesses can use a single, reliable framework to control and audit how their agents interact with sensitive information.

These benefits are expected to accelerate automation program outcomes through reduced custom integration overhead, greater resilience in agentic automations, and faster deployment of agentic use cases.

UiPath advantage in the MCP era: a comprehensive MCP offering

Aiming to bridge the gap from protocol to enterprise grade practice, UiPath coded and low-code agents leverage this new breadth of integration using the comprehensive UiPath Orchestrator MCP infrastructure. 

• UiPath MCPs: expose any set of UiPath automations via a built-in MCP server for agentic AI consumption. Leverage your existing automations to turbocharge your AI agent capabilities

• Coded MCPs: build and host custom-coded MCP Servers to ensure UiPath Agents are extensible via any custom tooling required

• Command-based MCPs: spin up an MCP Server from an external package feed by just running a command, geared towards using vendor approved MCP Servers towards their tools

• Remote MCPs: ensure UiPath Agents can connect to the remote MCP Servers of your choice

The MCP is a game-changer. It's the missing piece that transforms a smart conversational partner into a powerful, action-oriented agent. By providing a common standard for tool use and data access, MCP is accelerating the development of truly intelligent, connected, and useful AI.

Experience it firsthand and discover what it can do for you. It's available now for UiPath customers. Not a customer? Sign up for our free trial to use MCP and other UiPath Platform capabilities.

Pass this along to your developers: a tutorial on building a company policy chat agent using the capabilities discussed in this blog post.