MCP benefits for IT teams juggling dozens of systems

Say your IT team supports 5,000 employees across more than 40 systems. You connected an AI assistant to your ticketing platform so it could check request statuses. Now you want it to check whether someone already has access to an app. That requires a second connection to your identity provider.

Each connection takes work. And when another team wants a different assistant to use those same systems, you could end up rebuilding much of that work.

The Model Context Protocol (MCP) reduces that repetition. It gives AI agents and assistants a shared way to connect to tools and data. For an enterprise IT team managing dozens of integrations, that shared interface is one of the biggest MCP benefits.

Here’s what the protocol can simplify and what your team still needs to handle.

What’s MCP used for?

MCP is what lets compatible AI applications connect to external systems through a shared protocol. Anthropic introduced it as an open standard so developers wouldn’t have to build a different connection for every pairing of an AI application and a tool.

For IT teams, that might mean letting an assistant check a ticket’s status or start an approved access workflow. The assistant needs a connection to the system before it can perform either task. MCP provides that connection.

An MCP server makes a system’s capabilities available through the protocol. The server tells the assistant what it can request and handles those requests through the underlying system. One server’s job might be to expose ticketing operations, while another provides access to several connected applications.

Core components of MCP

An MCP server gives an AI application a few ways to interact with the systems it connects to:

• MCP tools: Functions an AI application can call to perform an action or retrieve information, like a tool that submits an access request or looks up a ticket
• Resources: Data the application can read for context, like internal policy documents
• Prompts: Reusable templates that help structure an interaction with an AI model

An MCP client inside the AI application handles communication with the server. IT teams configuring an assistant generally focus on the tools it can call and the information it can read.

Why point-to-point integrations break down at enterprise scale

Connecting a single assistant to one system is manageable. But supporting several assistants across dozens of systems means maintaining a lot more connections.

Imagine your enterprise uses 40 applications and five AI assistants. Building a separate connector for every pairing could leave you with 200 connections to maintain. Not every assistant needs every application, but each new pairing can still add development and testing work.

MCP reduces that repetition by letting compatible assistants use the same server interface. Your team still needs to configure access and test how each assistant uses the available tools. But you don’t have to rebuild the connection to your ticketing system every time another team chooses a different assistant.

For workflow automation, that means more of the work can go into defining how a request should be completed instead of repeatedly connecting AI applications to the same systems.

The key benefits of MCP for enterprise IT teams

When several teams use AI agents to work with the same systems, building each connection separately means repeating work. MCP gives those teams a way to reuse connections as they add assistants and bring more systems into reach. Here’s how.

Faster time-to-value than custom-built integrations

Say your security team wants its assistant to look up tickets through an MCP server IT already uses. With a compatible client, the team can connect to that server without writing another ticketing adapter.

The team still needs to configure authentication and test the assistant’s behavior. But an existing server lets them start with the capabilities already available instead of building the connection from scratch. If no suitable server exists, someone will still need to build and maintain one.

One protocol across every team, tool, and vendor

IT might use one assistant while HR chooses another. Meanwhile, both may need information from the same employee directory. With MCP-compatible applications, both teams can use the same server even if they rely on different AI models.

That gives teams more freedom to change assistants without replacing every connection underneath them. The protocol standardizes how applications communicate with servers. Each system’s API and available capabilities still determine what those servers can do.

Governance and security at scale

Reusing a server gives IT a shared place to enforce access rules across the assistants connecting to it. For example, an HR assistant could be allowed to read onboarding information while a security assistant can request changes to access.

Those permissions need to be enforced by the server and underlying systems. Sensitive actions should still follow the required approval process, and logs should let your team trace who initiated an action and what happened afterward.

MCP doesn’t supply all of those controls automatically. But it does give teams a consistent connection model that they can implement the controls around as more assistants join the stack.

How does MCP work?

To understand what goes on behind the scenes, let’s break down what happens when an employee asks an assistant, “Has my access request been approved?”

1. 1.The assistant identifies the right tool: Through its MCP client, the application retrieves the server’s available tools and their input requirements. A status lookup might require the access request’s ID.
2. 2.The client sends the tool call: It packages the tool name and request ID into a JSON-RPC message. That’s the structured format MCP uses for requests and responses.
3. 3.The server checks the underlying system: It handles the lookup using the caller’s authorized access and returns the result. The request might still be waiting for a manager’s approval.
4. 4.The assistant explains the result: It uses the returned status to answer the employee. Looking up the request doesn’t approve it or grant access.

The AI model chooses which tool to request. The server’s code carries out the operation and returns information the assistant can use in its reply.

Examples of MCP in practice

These MCP examples show how an assistant can move beyond answering questions to initiating work across IT systems.

Requesting temporary access

An engineer needs production access for two hours. Through MCP, an assistant submits a just-in-time (JIT) access management request. The access platform routes it to the engineer’s manager and security team for approval. Once approved, the platform grants access and handles automatic deprovisioning when the window ends.

Starting employee onboarding

HR asks an assistant to onboard a new hire. The assistant calls an MCP tool to start a published workflow that creates accounts across five connected systems. The workflow follows IT’s configured rules and pauses for any required approvals. The assistant can check its progress and report any steps that failed.

Resolving a repeat request

An employee reports a familiar VPN issue. An assistant retrieves ticket history through MCP and finds the previous resolution. If an authorized remediation tool is available, it can run the fix and confirm success before updating the ticket. Otherwise, the history gives IT a clearer starting point for investigation.

How Serval turns MCP into enterprise-ready automation

Serval is an AI-native ITSM platform with a native MCP server, so compatible AI assistants like Claude and Cursor can work directly with your Serval workspace.An assistant can check an access request or run a published workflow without your team building a separate API connector for it.

Depending on the caller’s permissions, assistants can work with:

• Tickets: Create and search tickets; update assignments and statuses; add comments and resolve requests
• Workflows: Discover and run workflows; create or edit automations with builder permissions
• Skills: Create or update help desk skills and review their version history
• Forms: Create, update, and publish forms
• Access management: Manage access policies and roles; submit requests through configured approval procedures
• Integrations: Inspect and configure connected applications
• Organization configuration: Administer teams and user roles with the required organization permissions

Connecting through MCP doesn’t give an assistant unrestricted admin access. Actions remain subject to the caller’s permissions and credential scopes. Running a workflow through MCP also preserves its approval requirements.

Serval’s native app integrations handle the work beyond its workspace. They’re separate from the MCP connection an assistant uses to interact with Serval. For example, a configured manager-change workflow could update an HR record in Workday and adjust Microsoft Entra groups before filing a ServiceNow ticket. The assistant starts the workflow through MCP, and Serval executes the configured steps across those systems.

Connect your next assistant without starting over

Your next AI assistant shouldn’t mean rebuilding connections to systems your team already uses. Serval combines an MCP interface with workflows that carry requests through to completion.

Book a demo to see how your team can connect assistants to Serval and run automation across your IT stack.

FAQ

What are the drawbacks of MCP?

MCP cuts down on connection work, but someone still has to maintain the servers and test the tools. Teams also need to address MCP server security risks like prompt injection and permissions that let an assistant do more than it should.

What does MCP stand for in AI?

MCP stands for Model Context Protocol, a name that describes the protocol’s job: connecting AI models to context from external systems. MCP also lets AI applications call tools to take action within those systems.