5 real-world Model Context Protocol integration examples

Anthropic’s Model Context Protocol (MCP) can play a key role in enabling your AI agents to interact with 3rd-party applications, whether it’s your organization's apps or your customers’.

To help you pinpoint and prioritize the top use cases for your AI agents, we’ll break down several impactful MCP examples. 

But first, let’s align on the basics of the Model Context Protocol.

How MCP works

In simple terms, MCP provides a standardized way for LLMs to interact with external systems.

The protocol consists of several components:

• Your AI agent’s underlying large language model, which processes requests, decides when to make tool calls, and determines the tool calls it needs to make

• An MCP client, which translates the LLM's tool requests into properly structured tool calls

• An MCP server, which connects to 3rd-party applications and exposes their data and functionality via tools 

Related: How RAG compares to MCP

Impactful MCP examples

Here are just a few ways to leverage MCP.

Power intelligent help desks 

Say you offer an ITSM solution that helps IT teams manage and complete requests from colleagues.

To help them navigate equipment and device-related issues faster and more easily, you can build an AI agent that uses MCP as follows:

1. A customer’s employee accesses your AI agent in an app like Slack.

2. They tell your AI agent that they need help with a certain task.

3. Your AI agent can gather more context, such as asking the user to fill out a form if they lost a device.

4. Once they complete the form, your AI agent makes a “Create ticket” tool call in the customer’s project management platform. That ticket can include details on the issue, such as the affected employee, a description of what’s happened, and the level of priority for resolving it.

Related: RAG examples worth implementing

Enable recruiters to source high-fit candidates

Imagine you offer a recruiting automation platform that helps recruiters find strong candidates for any open role.

To ensure your platform provides highly personalized and strong candidates for a given role, you can power the following MCP use case:

1. A customer types in a request for a certain open role, like “I’m looking for senior engineers in the Bay Area who have built search infrastructure in the past.”

2. Your AI agent can then make a tool call(s) in the customer’s ATS to fetch candidate data from similar open roles. It uses this context to better understand the types of candidates the customer typically favors based on how far along they get in the interview cycle.

3. Your AI agent can then use this context with your internal database of talent to surface the best candidates.

Help finance teams negotiate with vendors 

Now say that you offer a procurement solution that helps customers manage vendor relationships. As part of this, you can support them when they’re negotiating vendor renewals via an AI agent. 

Here’s how MCP can fuel your AI agent.

1. Once a customer clicks something along the lines of “Start Renewal” for a given vendor in your product, your AI agent makes tool calls to the customer’s email service provider (ESP) and ERP system. Through these tool calls, your AI agent can retrieve previous emails with the vendor as well as the current and historical terms of engagement.

2. Your AI agent can use this context, along with the negotiation best practices it’s trained on, to provide a recommendation on how the customer should proceed with the renewal.

3. As the customer continues to negotiate with the vendor, your AI agent will continuously make tool calls to the customer’s ESP to stay up to date on the negotiation and provide timely recommendations. Your AI agent can even take actions on the customer’s behalf, such as drafting an email.

Automate expense reviews across companies

If you offer a corporate card solution, you can use an AI agent—and your own MCP server—to empower finance teams to avoid reviewing and approving (or rejecting) routine expenses.

Here’s how it can work:

1. Any time an employee uses a corporate card for a transaction in a certain category (e.g., home office equipment), it gets routed to your AI agent.

2. Your AI agent can make a tool call to the customer’s instance of your product to determine the employee’s policy for that expense category, how much funding they have available, and whether a receipt is required.

3. Based on these insights, your AI agent can approve the transaction or decline it and flag it for review by finance.

Related: Examples of integrations for AI agents

Streamline busywork for financial analysts

Finally, say you offer an FP&A platform that helps finance teams understand and act on their transactional data, quickly.

To help streamline this work, you can offer an AI agent that cleans, enriches, and organizes accounting data in a spreadsheet or within your product (wherever your customers prefer to work).

Here’s how your AI agent can work:

1. As your customers review their accounting data, they can make a request to your AI agent, such as asking it to roll accounts up into certain categories (e.g., Revenue). 

2. Your AI agent can make a tool call to that customer’s ERP system to get their historical accounting data. This allows your AI agent to understand how the customer has rolled accounts up into categories in the past.

3. Based on the patterns it’s detected, your AI agent can propose certain mappings—which your customer can go on to accept or reject.

When to use (and avoid) MCP

Just because MCP can support several use cases doesn’t mean it should.

In many cases, syncing full data sets via recurring API calls can make more sense.

Here are some general guidelines for deciding when to use MCP.

Related: The challenges of using MCP servers

Use MCP for on-the-fly decisions

MCP excels at on-the-fly decisions because it enables your AI agents to dynamically choose and execute the right actions based on real-time context, rather than following pre-programmed workflows. 

Use MCP for ad hoc looks up that require current data

Your AI agents can also use MCP to help customers quickly access the latest data points across systems, whether it’s the status of a purchase order in their ERP system or an employee’s time off balance in their HRIS.

Avoid MCP for static workflows

There’s no reason to over-engineer processes that follow the same set of steps over time. 

Direct API connections mitigate the security risks of using MCP and are more reliable, as you aren’t exposed to the unpredictability of AI-driven decision making.

Common workflow examples can take the form of lead routing, employee onboarding, invoice processing, and more.

Avoid MCP for enterprise search

⁠​In order for enterprise search products or features to work effectively, they need to use semantic search—which uses the intent behind a user’s search to provide more comprehensive and actionable insights.

For example, if a sales leader is asking your enterprise search product about the status of a certain opportunity, they don’t just want to know what the opportunity stage is; they want to know about the current dollar value assigned to the opportunity, when it’s expected to close, what the next steps are, along with other helpful context.

Since MCP servers just use underlying APIs (and not an aggregated search index that’s stored in your servers), MCP-based integrations can only power exact or basic string matching. 

This can help you offer an enterprise search experience that answers questions directly. Using the example earlier, you can provide the status of a specific deal; but no additional, potentially helpful information will be shared.

To power enterprise search, you’ll need to fully sync your data. Here’s more on why that’s the case.

Access hundreds of tools in minutes via Merge MCP

Merge, the leading Unified API solution, offers an MCP server (Merge MCP) that lets you access and interact with data across your customers’ tech stack. This includes everything from file storage systems to HRISs to accounting solutions. 

Merge also supports a full suite of integration observability features and functionality, including automated issue detection and fully-searchable logs, to help you quickly and easily address any integration issue.

Learn more about Merge and Merge MCP by scheduling a demo with an integration expert.