MCP moves AI from writing copy and generating images to doing actual work. Through the protocol, the model gets access to your internal data and your business systems, and it completes the task right there. A content tool becomes a digital colleague.
Model Context Protocol is an open standard that defines how a language model connects to other systems. Think of it as a USB port for AI. One connector for your CRM, your database and the rest of your tools.
This was possible before MCP too, but every connection had to be coded separately, which turned it into yet another development project. With MCP it comes down to configuration.
MCP was released as open source by Anthropic in late 2024. During 2025 it was adopted by OpenAI, Google DeepMind and Microsoft, and more companies keep joining.
Connectors built on MCP are now a standard part of AI tools and setting up a personal agent is something almost anyone can do. A few clicks connect Claude to Google Drive, Microsoft 365 or Asana.
That is what turns an ordinary language model (LLM) into an AI agent. The protocol effectively gives the model hands, and with them access to other systems. You set the goal and it works out the individual steps on its own. How much independence it gets is your decision.
Rolling an agent out across a company differs from personal use in one area, and that is access control. With a personal agent, people pick their own tools from a list of connectors and answer only to themselves. An agent serving a whole department needs clear rules. Who may reach which system, what the agent is allowed to change, and when it has to wait for a human to approve.
MCP is not AI. The protocol does not decide, reason or act. It is only the link between the model and the system. What needs to happen is judged by the model itself (Claude, GPT). The protocol simply hands it the tool to do it with. If the model reads the request wrong, MCP will not save you. A USB cable will not stop you copying the wrong file onto your computer either.
MCP does not replace RPA. A software robot runs the same sequence the same way every time, and at high volume the cost per transaction is far lower. It sticks to exact rules, though, so an exception or a vaguely worded request leaves it stuck. That is where the agent comes in. In practice the two technologies are combined. The robot handles the routine, the agent decides what happens when the process steps outside the usual scenario.
The architecture breaks down into three parts: the client, the server and the transport.
The client is the application running the model, such as a chat interface or an agent in UiPath.
The server is a layer built on top of a specific system, a CRM for instance. It tells the model everything that can be done in that system, such as find a customer, open a ticket or change an address. It then translates the model’s request into language the system understands.
The transport is the connection between the client and the server. It either runs locally on one machine or remotely over the internet.
Tools are actions, such as create a ticket, update a record or send an email. The model calls them when it needs them to finish a task.
Resources are data to read. The contents of a document, a row from a database, a log. The model asks for them when it needs context.
Prompt templates are ready-made procedures for recurring tasks. Instead of every employee inventing their own instructions, they get a tested one.
One thing sets MCP apart from a standard API integration. The server describes itself, both what it can do and what it needs to do it. When a new function appears in the system, the model knows about it immediately and nobody has to rewrite any code.
Platforms such as UiPath let you expose existing automations as an MCP server. RPA robots a company has spent years building become tools an AI agent can call.
If some systems run on-premise and are not exposed to the internet, UiPath Relay makes them reachable for cloud services, without any change to your network infrastructure.
MCP servers are managed centrally, through the same roles and permissions mechanism as the rest of the platform. That lets a company define exactly where the agent may go and where it may not.
Customer service
A complaint arrives by email. Through MCP the AI agent pulls the order history from the CRM, checks the shipment status with the carrier, verifies the warranty and drafts a reply with a proposed resolution. The operator approves it or edits it. Instead of five open windows, there is a finished draft.
Insurance claims
The agent reads the claim, opens the policy, compares the coverage against the reported damage and sorts the case into a category. Clear-cut cases go for automatic approval. Complex ones are prepared for a claims adjuster, supporting documents and a proposed outcome included.
Internal requests
An employee requests access to a new system. The agent checks their role in the HR system, reviews the access policy, creates a ticket and notifies the approver. A human only steps in when the request falls outside the rules.
All three examples share the same shape. The request arrives in natural language and the answer needs data from several systems. For a process with fixed rules and a consistent structure, classic RPA remains the better choice. Where the inputs keep changing, a combination of the two makes sense. The term that has stuck for it is agentic process automation (APA).
An agent with access to a company CRM and ERP is an attractive target for attackers. There are more risks than that one, though, and all of them need addressing before the first deployment.
Scope of permissions
The server should have access and rights only to what the agent genuinely needs. If it only reads orders, it does not need the right to delete records.
Prompt injection
People feed the model emails, documents and content from the web. Any of these can hide an instruction dressed up as a legitimate request. Inputs from external sources therefore belong in a permission category of their own.
Approval for sensitive steps
These are the irreversible operations, such as sending a payment or deleting a record. A human should always confirm them.
Audit
Every tool call needs to be logged. Without records an incident cannot be reconstructed, and the company also fails the requirement to demonstrate how it handles personal data.
Vetting servers
Use servers from trusted sources only. Public registries of MCP servers are multiplying fast, but the quality control behind them is lagging.
Pick one process that meets three conditions: it runs daily, it touches two or three systems, and it has a measurable output, usually time or error count.
The most common reason these projects fail is not the technology. It is a badly chosen process.
Do we need MCP if we already have API integrations?
API integrations are hard-coded links between two specific systems. MCP exposes a system’s capabilities so that any AI client can use them. When a new function is added to the system, AI can start using it straight away.
Will agents replace RPA?
No, robots remain the right fit for rule-bound processes. Thanks to MCP, agents can also handle changing inputs, and they can drive existing RPA robots directly.
How long does implementation take?
A starter project on a single process usually takes eight to twelve weeks, measurement included. If the process is poorly documented or the target system has no usable API, it takes longer.
Is an MCP connection safe for sensitive data?
The protocol itself does not handle security, it only defines how communication works. Protection depends on how permissions, authentication and auditing are set up in the implementation. In a governed environment such as UiPath, the platform provides these mechanisms directly.
Which systems can be connected?
Servers already exist for common business tools such as Microsoft Dynamics 365, databases and ticketing systems. Internal systems with their own APIs need a custom server.
If a single process is eating up dozens of hours a month, get in touch. We will take a look and tell you straight whether an agent fits it, or whether classic RPA is the better call.
