As shown above, each service (e.g. a database, internal tool, or SaaS platform) can be wrapped with an MCP server that speaks a unified protocol. Your LLM-powered app connects to these servers via standard interfaces, which makes it easy to teach the AI new capabilities without custom glue code.

MCP clients and servers communicate via two protocols: STDIO and SSE.

• STDIO is simple and ideal for local or small-scale development, especially for quick prototyping or CLI-based tools.
  
• SSE (Server-Sent Events) is better suited for production and scalable environments, enabling real-time, persistent HTTP connections where servers can push updates to clients.
  

For our projects, we use SSE to support scalable, long-lived connections over HTTP.

Implementing MCP

To use MCP in your application:

1. Deploy or Build an MCP Server – Connect it to your target service (e.g., calendar, Trello, file system). We recommend using SSE to support scalable, persistent connections for your MCP clients.
   
2. Expose Capabilities – Define and register tools, resources, and optionally prompts on the server. These expose what your application can access or execute.
   
3. Extend as Needed – Open-source MCP servers are a great starting point, but you may need custom capabilities. Creating new tools and resources allows your server to serve your unique use cases.
   
4. Connect the MCP Client – Point your client at the MCP server endpoint. The MCP client can now list tools, call them, and read structured results.
   
5. Enrich LLM Prompts – Fetch or generate contextual data via the MCP server, and include it in structured prompts sent to the LLM to guide its reasoning or behavior.
   
6. Test Your LLM Setup – Your AI application is only as strong as the model it uses. Ensure the LLM you’re using can understand and act on tool blocks and structured responses effectively.

This modular pattern allows you to reuse and share integrations, build multiple MCP servers for different services, and confidently scale your AI application over time.

There are already 10,000+ MCP servers available across the community today. While many currently run on local environments (e.g. a developer’s laptop), we’re now seeing a shift toward cloud-hosted MCP servers, enabling more scalable, persistent, and production-grade deployments, without you having to manage your own MCP cloud infrastructure. 

What Services and Use Cases Can Benefit from MCP?

As AI continues to evolve, the need for LLMs to operate with real-world context has become increasingly important. MCP is already enabling smarter, more contextual AI across a variety of domains—by making it easier to connect language models to the tools, data, and workflows they need to interact with.

Here are some of the ways organizations are benefiting from MCP today:

🔧 Internal Tools & Automation

Businesses can connect their internal APIs, services, and databases to LLMs using MCP. This allows employees to ask questions like:

“What were sales numbers last quarter?”
“Can you generate the end-of-month report from our internal tool?”

“What does my calendar look like next week?”
“Summarise my emails from yesterday”

Instead of manually querying systems or writing code, the LLM can call tools and access data directly through MCP. This turns the LLM into a powerful internal assistant that works with your existing stack—without needing custom code for every interaction.

📊 Business Intelligence and Analytics

MCP also enhances how teams interact with BI tools and dashboards. By exposing logs, query results, and reporting functions as resources and tools, the LLM can interpret and summarize business performance metrics on demand.

For example, an analyst might ask:

“Compare customer churn in Q1 vs Q2.”
“Pull the top 5 customer complaints from the last month.”

This eliminates the need for deep SQL knowledge or dashboard digging—freeing up time and making insights more accessible across teams.

🧠 Knowledge Management & Documentation

LLMs become significantly more useful when they’re able to reference the most up-to-date information. With MCP, documentation, internal wikis, policies, and code logs can all be surfaced as resources. This allows the model to answer technical, legal, or operational questions with the most current and company-specific knowledge.

Think of it as giving your AI assistant real-time access to your entire knowledge base—without retraining or hardcoding data into the model.

🤖 Building Smarter LLM-Powered Apps

If you’re developing an AI-powered application—like a support agent, coding copilot, or process automation bot—MCP simplifies how you teach that app to interact with external systems. You can expose functionality as tools, structure inputs via prompts, and stream in relevant resources on the fly.

This reduces development effort, promotes reuse, and supports more modular, composable AI application architectures. It also opens the door to multi-agent systems, where multiple LLMs with their own MCP servers can collaborate on complex workflows by sharing structured context.

🔐 Security & Access Control: A Growing Priority

As MCP adoption increases and integrations begin to handle sensitive or proprietary data, security and authentication are becoming critical components of the protocol.

Whether you’re connecting an LLM to HR data, customer information, or finance tools, it’s essential to ensure the model only accesses what it’s authorized to see—and nothing more.