MCP Use Cases – The Top Use Cases For MCP and MCP Servers
Use cases for the model context protocol (MCP) are numerous and varied. MCP servers enable AI models to connect to and interact with a wide range of internal and external digital resources, including applications, databases, services, and systems.
These connections allow AIs to complete tasks and processes across a range of systems while linking data and context from disparate resources together. In practice, this means your teams are freed from burdensome tasks and gain information, intelligence, and insights at unprecedented speed.
I’ll explore MCP use cases in several ways. Firstly, I’ll give you a sample of the range of MCP servers already in existence. Then I’ll explain the core use case types for MCP, with illustrative examples from different functions and industries.
After reading this article, you’ll have a rich understanding of what MCP can do and what you and your various teams can use it for.
What MCP servers have people launched?
At the time of writing, there are over 5,500 publicly published MCP servers. From finding flights to analyzing medical records, coding to choosing the best time to surf, or building the best fantasy football team, MCP server builders have explored a vast range of uses for MCP servers.
It’s worth noting that this only includes publicly published MCP servers, not those created by organizations for internal use.
The list below provides only a small, illustrative sample of the various types of MCP servers that are available.
- GitHub MCP: Repository management and code analysis
- Hubspot MCP: Marketing automation, marketing performance analysis, and contact management/CRM
- Notion MCP: Creating and searching pages and databases in Notion
- Google Flights MCP: Real-time flight searches
- ESPN Fantasy MCP: Player stats, match predictions, and pick recommendations
- Jira MCP: Issue tracking, sprint planning, and other agile project management planning and reporting
- Weather MCP: For weather forecasts
- Stripe MCP: Payments processing, analysis, and alerts
- Slack MCP: Coordinating Slack chat activity with related processes, such as task updates in project management systems
- Surfline MCP: Wave forecasts and ideal times to hit the beach
- Kayak MCP: Comparisons of flight and hotel prices and packages
If you need a specific type of MCP server, check out directories such as PulseMCP or Glama.ai.
Based on the number of downloads, software engineering and IT use case MCP servers were by far the most popular server type in 2025. This is because MCP servers require a relatively high level of technical expertise and know-how to deploy – unless you’re using a platform that makes deployment easy (such as MCP Manager).
In 2026, we expect teams across every business function to adopt MCP servers. This is because MCP is so versatile. It’s a protocol that connects AI agents to any resource, and multiple resources simultaneously, making it applicable to a vast array of automotive, reporting, and information-gathering use cases. I explore these various use cases for MCP in more detail below.
What are the main categories of MCP use cases?
Despite the great variety of use cases for MCP, we can categorize most of them into one of these buckets:
- Context-aware process automation
- Decision support systems
- Data integration and knowledge management
- eCommerce
Of course, some of these use cases overlap. The great thing about MCP is that it enables AI models to complete multi-step workflows that can include a dynamic mix of planning, research, analysis, creating deliverables, and executing tasks.
Below, I’ve provided an overview of the MCP use case categories, with illustrative, real-world examples from various industries and teams.
Context-Aware Process Automation
Imagine you want to automate a complex process. Without AI, you need to create a rule-based workflow proactively. These rule-based workflows can handle a wide range of scenarios. However, some processes are so context-dependent that rule-based workflows are unfeasible – the variability can’t be boiled down into predetermined rules and logic.
AI-driven workflows are different. AIs can adapt decisions to the specific context of each instance. AIs can also react to new scenarios autonomously and proactively, acting like assistants rather than preconfigured machines.
To dynamically and autonomously orchestrate complex, multi-step, context-dependent workflows, AIs need access to tools, data, and other resources. MCP servers provide this access.
Here’s a demonstration from Microsoft showing how you can use MCP connected AIs to track Jira tickets and manage GitHub pull requests in a software development workflow:
Examples of MCP Use Cases for Complex Task Automation
MCP connectivity gives AI access to the tools it needs to automate those processes that are repetitive, but not fully repeatable – those processes that follow the same steps each time, but have too much variation and nuance involved to automate them with rule-based automation.
For example, prior authorization processes for health insurance claims are fairly repetitive. However, each instance of the prior authorization process requires a context-heavy, holistic understanding of the person’s diagnoses, insurance, medical history, and how these factors interrelate.
You can’t create a predetermined branch-and-rule-based workflow to automate this process. Conversely, an AI can use context from all relevant sources to complete the process, make recommendations, and flag considerations and concerns.
MCP-connected AIs can also automate work that is not part of an established process. Imagine you and your team arrange a project planning meeting. Your AI assistant can join the meeting, and then use its accumulated context and understanding to complete a range of sensible follow-up actions, including:
- Creating a project plan with tasks in your project management system
- Writing and emailing a summary of the meeting and next steps to stakeholders
- Preparing a draft PowerPoint presentation to present your project plan to stakeholders
- Scheduling project review meetings at regular intervals
- Creating a project management dashboard with KPIs
Here are some other examples of complex automation use cases for MCP:
- Payments management: An AI agent connected to multiple MCP servers can manage invoice creation and sending, chasing late payments, processing payments, and maintaining detailed transaction logs.
- SaaS customer onboarding: AI agents can use MCP connections to deliver bespoke onboarding for each customer, assisting with configuration, maintaining your CRM, and providing responsive learning as and when required.
- Supply chain exception handling: When delays occur, the AI cross-references inventory from the WMS, supplier status from vendor portals, demand forecasts from sales analytics, and logistics trackers (across diverse MCP servers) to reroute shipments, notify procurement/ops teams, update production schedules in the ERP, and proactively adjust forecasts.
- Detailed marketing attribution: Marketing teams using MCP servers can replace blunt attribution models with AI-driven attribution that leverages MCP connections to various lead-tracking systems to make a more realistic assessment of what actually led to conversion.
- Talent acquisition: Using various MCP servers to synthesize ATS interview feedback, skill gaps from HRIS, candidate profiles from LinkedIn/recruitment platforms, the AI ranks applicants, generates personalized offer letters, coordinates references, and schedules interviews.
- New talent onboarding: When you hire a new employee, an AI can connect to various MCP servers to orchestrate the necessary and time-consuming onboarding processes, including setting them up on your internal systems, benefits selection, and initial meetings, so your HR and hiring teams can spend more time on valuable conversations with new employees and candidates.
- Automated ABM: The AI can access your marketing automation platform, CRM, and external data sources to orchestrate bespoke marketing campaigns to all your accounts, including selecting the best messaging for the right contact, and modulating contact frequency and messaging based on contact engagement. This enables you to do account-based marketing at an unprecedented scale without a proportionate increase in costs.
Decision Support Systems
People in a wide range of roles spend a large part of their working hours finding and analyzing data to inform all kinds of decisions, from where to spend marketing budget to hiring additional salespeople, or even determining the best course of treatment for a patient.
When you connect AIs to resources via MCP servers, they can take on the laborious task of querying relevant sources to quickly deliver the information you need to make better, more informed decisions.
The AI can traverse the various internal and external data sources via MCP connections to provide answers to precise queries and prompts, such as:
- How do conversion and revenue per customer rates compare for different marketing channels?
- Which teams have the best employee retention rates – segmented by seniority? What common characteristics do these teams and their managers have?
- What are the common characteristics of the most overdue support tickets?
- Which usage patterns best indicate propensity to upgrade?
- Recommend investment levels across our digital marketing campaigns, with projections for leads generated and ROI (based on average customer value data by channel).
- Are there any reported side effects of this medication for this patient (based on their medical history)?
- Which of our email marketing workflows have generated the most conversions from director-level contacts in finance and banking? What are the potential reasons for this?
Here are some examples of how AIs connected to MCP servers can act as decision support systems:
- Marketing budget allocation: Assessing the performance – and projected performance – of marketing channels and tactics, to provide information and recommendations to optimize budget allocation.
- Supply chain and inventory management: An LLM connected to ERP, WMS, and other MCP servers can use volume forecasting, staffing data, targets, and other information to recommend where best to maximize staffing, which orders to prioritize, and where backlog-clearing is needed.
- Ticket triage and escalation: An LLM connected to service management MCP servers can read support tickets, estimate effort and time required, and recommend prioritization and routing.
- Financial planning and assessments: An AI can use various MCP servers to discover transaction histories, combine this with external risk feeds, and assess against internal policies to provide information to support credit assessments and other financial services.
Here’s a quick demo of Alpha Vantage’s MCP server, used to provide financial data to support traders’ decision making:
Here are some examples of the insights marketers can uncover using Hubspot’s MCP server:
Data Integration and Knowledge Management
We all know that LLMs are great at answering questions. They are prone to errors and hallucinations, still, we are all increasingly using LLMs to find information and get clear answers to straightforward queries.
MCP servers enable you to connect your LLMs to internal knowledge bases, third-party support portals for apps you use, online forums, documentation, support portals, and other resources.
This provides your teams with a direct, always-on support assistant that is much more effective than existing built-in support chatbots, which typically can only answer a very narrow range of queries using preset decision trees and templated responses.
LLMs connected to MCPs can behave more like a helpful colleague, providing genuine and specific answers to questions, step-by-step instructions, and real-time troubleshooting.
eCommerce
In late 2025, the MCP maintainers announced they are exploring the development of “MCP Apps”. MCP Apps allow for the injection of external UI elements into the MCP host’s UI. This means you can view and use components such as payment modals, webforms, app modals, task cards from project management apps, and a range of other interactive elements, all without leaving your chatbot’s UI.
For e-commerce, MCP Apps will enable AIs to complete the buyer journey on behalf of, or in cooperation with, their user. Instead of doing basic initial research via ChatGPT, then being forced to click out to a vendor’s website, the entire process is conducted in your chatbot’s UI, with it orchestrating and assisting with each step.
Perhaps as early as 2026, consumer-focused use cases for MCP will become much more important, for example:
- Evaluating and selecting niche products: Finding and purchasing the best pair of running shoes for your high arches and wide feet
- Comprehensive travel bookings: Including flights, hotels, and transportation, with optimal pricing and scheduling
- Finding a great Broadway show you want to see and selecting the best available seats based on your preferences and budget.
- Subscription management & optimization: Finding and selecting the best prices for various types of subscriptions – such as cellular plans and energy suppliers – and managing these subscriptions for you
- Finding the best deal: Searching various vendors for a specific product – such as a type of car – and finding the best offer, perhaps even negotiating with multiple suppliers on your behalf to get the best deal.
- Tedious paperwork: Completing the various data collection forms for certain purchases and bookings
- Product demonstrations: Finding SaaS products that fit your requirements, scheduling demonstrations, and perhaps even attending the demonstration on your behalf and reporting back to you
Many of these use cases become more powerful as LLMs and MCP servers know more about us and our preferences. This raises considerations regarding data privacy for MCP traffic. Organizations should use platforms like MCP Manager to maintain regulatory compliance across all MCP-based data flows.
MCP Use Cases – What Will You Try First?
After reading this article, you have probably realized that MCP – and MCP servers – are highly versatile technologies. Use cases for MCP are best thought of as overarching capabilities that you can apply – to varying degrees – to any process or task.
Therefore, you should use this article as a jumping-off point. Take the core use cases of context-aware process automation, decision support systems, and data integration, and consider which of your existing processes you could improve through MCP connections and AI agents.
However, as you proceed, you will soon learn that adopting and using MCP servers at scale requires a platform to manage and secure them. Indeed, you may already be aware of this.
MCP Manager is a platform that provides a comprehensive, central control plane to deploy, secure, manage, and monitor your MCP ecosystem at scale, enabling you to achieve measurable ROI from your AI initiatives faster, more securely, and in a controlled manner.
