MCP – Real World Use Cases

​The Model Context Protocol (MCP) is an open standard designed to facilitate seamless interaction between AI models and external tools, data sources and services. We’ve talked about it our earlier article – Model Context Protocol – Why. To have a better grasp on why we consider MCP as innoa+ive, we’ll use some real-world applications demonstrating MCP’s versatility across various domains.

Healthcare: Enhancing Diagnostic Precision

In the healthcare sector, MCP enables AI systems such as the Diagnosis Copilot Agent by Innovaccer to integrate diverse data sources—such as patient records, lab results and imaging studies—into a unified context. This integration allows for more accurate diagnostics and personalized treatment plans.

For instance, Diagnosis Copilot Agent can analyse patient symptoms and medical history to suggest potential diagnoses, aiding physicians in clinical decision-making. ​

Enterprise Assistants: Streamlining Internal Operations

Companies like Ardoq has integrated the Model Context Protocol to bolster its capabilities in aligning IT systems with business objectives. This setup allows users to interact with Ardoq by simply asking questions or give commands via AI assistants like ChatGPT instead of manually navigating menus or running queries.

For instance, users can ask in plan language like “What’s the impact of removing Salesforce?” ​and receive the answer as “Removing Salesforce affects x number of business capabilities, y number of processes and z number downstream integrations”.

Software Development: Intelligent Coding Assistance

Development platforms such as Cody by Sourcegraph utilize MCP to provide AI-driven code suggestions and documentation retrieval. By connecting AI models to code repositories and documentation databases, developers receive context-aware assistance, including code completions, bug fixes and relevant documentation snippets, enhancing productivity and code quality.

​For instance, developers can now get GitHub or Linear issues, connect to their Postgres database, and access internal documentation without leaving their IDE

Personal Productivity: AI-Powered Desktop Assistants

Applications like Claude Desktop leverage MCP to interact with local file systems and applications. Users can instruct AI assistants to perform tasks such as opening files, summarizing documents or organizing folders through natural language commands, streamlining daily workflows and reducing manual effort.

For instance, users can use natural language queries like “Summarize the key findings from all the PDF reports in the Market Analysis folder” to achieve efficient information retrieval; “Update the sales figures spreadsheet with data extracted from recent reports” to achieve local application interaction, and more effective interactions with local file systems and applications.

Web and Browser Automation: Enhancing Online Interactions

Traditionally, if an AI needed to “use” a website, it was limited to sending API calls if the website offered them, or relying on screen-scraping techniques that were often brittle and prone to breaking with layout changes. This meant AI couldn’t truly “browse” or “understand” a webpage like a human. MCP acts as the crucial bridge, allowing an LLM or other AI model to connect with and control browser automation tools like Puppeteer (for Chrome/Chromium) or Selenium (for various browsers). It enables the AI to understand the webpage context, formulate intelligent actions and execute complex workflows.

For instance, An AI agent using MCP can periodically visit a list of news sites, blogs or e-commerce platforms, intelligently identify article bodies, review sections or specific data elements, extract the content, clean it and aggregate it into a database or document.

Emergency Preparedness: Real-Time Crisis Management

In the face of emergencies (e.g., natural disasters, public health crises or security threats), timely, accurate and actionable information is paramount. The traditional approach often relies on static documents, complex protocols, and human operators who may be overwhelmed. Projects like SafeMate using MCP to enable AI assistants to deliver real-time, context-aware guidance in high-stakes situations, significantly enhancing public safety and response effectiveness.

For instance, if people are near a developing bushfire, SafeMate provides evacuation guidance during a bushfire. The AI receives the user’s GPS location. It accesses real-time fire maps, official CFA (Country Fire Authority, Australia) alerts, and pre-loaded local evacuation plans. It then provides clear and location-specific instructions, reduces confusion and panic and guides people to safety more efficiently.

Multi-Agent Systems: Coordinated Problem Solving

MCP provides the “common language” and “shared whiteboard” that allows diverse AI agents to seamlessly exchange and interpret contextual information. It ensures that when one agent processes data or makes a decision, the relevant context is packaged and communicated in a way that other agents can immediately understand and act upon.

For instance, a global manufacturing company needs to optimize its supply chain in real-time, reacting to fluctuating demand, geopolitical events and raw material availability. A Multi-Agent Systems (MAS) can bring the following together:

• Demand Forecasting Agent: Analyses sales data, market trends and external economic indicators (e.g., using SAP S/4HANA data or external market APIs) and shares demand forecasts.
• Logistics Optimization Agent: Receives forecasts (via MCP) and real-time shipping data (e.g., from FourKites or Project44), identifying optimal routes and transport modes, sharing potential delays.
• Supplier Management Agent: Monitors supplier performance, raw material prices and inventory levels (e.g., integrating with Oracle Supply Chain Management Cloud), identifying alternative suppliers or negotiating better terms and sharing status updates.
• Production Planning Agent: Takes inputs from demand, logistics and supplier agents (via MCP) to dynamically adjust production schedules across different factories.
• Risk Assessment Agent: Continuously monitors geopolitical news, weather patterns and financial markets, alerting other agents to potential disruptions and proposing mitigation strategies.

There are many other real-life use cases that employ MCP to access relevant context dynamically, which significantly enhances their effectiveness. MCP’s innovation lies in its standardized approach, enhancing the AI’s capacity to provide precise, timely and contextually relevant outcomes across diverse sectors, paving the way for smarter, more responsive AI-driven solutions.