Understanding MCP and how it’s changing what AI can actually do for your business
If you’ve been paying attention to the AI space, you’ve probably noticed that today’s most capable AI systems share a frustrating limitation: they’re remarkably intelligent but fundamentally isolated.
Your AI assistant can write eloquent prose, analyse complex data, and reason through intricate problems – yet it can’t check your calendar, query your database, or interact with the tools your business actually runs on.
This is precisely the problem that the Model Context Protocol (MCP) was designed to solve.
Introduced by Anthropic in November 2024, MCP provides a standardised way for AI agents to interact with external tools, APIs, and data sources. Think of it as the USB standard for AI integrations, a universal connector that lets AI systems plug into the real world.
What Exactly is MCP?
The Model Context Protocol is an open standard that enables AI agents to autonomously interact with external systems.
Rather than requiring developers to build custom integrations for every tool-AI combination, MCP provides a common interface that any AI client can use to communicate with any compatible server.
The simplest way to understand MCP is through an analogy. Before USB, every device needed its own proprietary connector. Printers, keyboards, cameras—each required a different cable and port.
USB changed that by creating a universal standard. MCP does the same for AI: it creates a universal way for AI systems to connect to any tool or data source.
The architecture has three key components. MCP Clients are AI applications like Claude Desktop or Cursor IDE that want to use external tools. MCP Servers are lightweight programs that expose specific capabilities—database access, API integrations, file operations, or anything else you can imagine.
The Protocol is the standardised language they use to communicate.
What makes MCP powerful is that AI agents can chain together multiple tools, make decisions based on intermediate results, and execute complex multi-step workflows (all autonomously).
Where MCP Delivers Real Value
MCP’s flexibility means it can be applied across virtually any domain where AI needs to interact with external systems. Here’s where we’re seeing the most immediate impact:
DevOps Automation
MCP enables AI agents to manage software delivery through natural language:
- Triggering CI/CD pipelines based on code reviews and test results
- Managing container deployments across Kubernetes clusters
- Orchestrating rollbacks when monitoring detects anomalies
- Coordinating notifications across Slack, Teams, or email based on build status
Network Operations
Network teams can leverage MCP for intelligent infrastructure management:
- Natural language-driven network configuration changes
- Automated anomaly detection using ThousandEyes or Meraki integrations
- Dynamic routing updates based on traffic patterns
- Intelligent troubleshooting that correlates data across monitoring systems
Security Operations
Perhaps the most compelling enterprise use case is security automation:
- Proactive threat mitigation through automated endpoint isolation
- Intelligent patch management coordinated with Ansible or similar tools
- Automated firewall rule updates in response to detected threats
- Incident response workflows that coordinate across security tools
Business Operations
Beyond technical operations, MCP enables AI to participate in everyday workflows:
- Automated file organisation and document management
- Intelligent email composition based on CRM data
- Research automation that synthesises web sources and internal documents
- GitHub and project management coordination
- Communication management across platforms including WhatsApp and Slack
What the Industry is Saying
The reception from technical leaders has been overwhelmingly positive.
Brian Julius, a Power BI expert who built an Obsidian integration with MCP, describes it as “the standard for hooking tools to AI models,” noting how it “supercharged” his workflows by enabling Claude to autonomously retrieve context from past interactions.
Sam McKay, CEO of Enterprise DNA, frames MCP as a “bridge to external services for live context,” emphasising how it enhances AI reasoning by grounding it in real-world data.
Analysts at a16z have drawn compelling parallels to how APIs unified the internet, suggesting that MCP could similarly unify AI-tool interactions. Their analysis envisions MCP clients becoming “everything apps” that orchestrate capabilities across Slack, email, image generation, and countless other services through a single, coherent interface.
Building Your First MCP Server: A Practical Overview
Let’s demystify what it actually takes to build an MCP server. We’ll use a practical example: creating a daily news aggregator that fetches and summarises news on any topic you choose.
The Concept
An MCP server is simply a program that exposes “tools” to an AI client. Each tool is a capability—something the AI can do. For our news aggregator, we might create tools like “list available news sources,” “fetch articles from a source,” and “search for articles on a topic.”
Once connected, you can ask your AI assistant things like “Give me a summary of today’s top AI news” or “What’s happening in the renewable energy sector this week?” The AI uses your tools to fetch the data, then applies its intelligence to synthesise and present it.
What You Need
Building an MCP server requires three things:
- An MCP Client: Claude Desktop and Cursor IDE are the most accessible options. These are the AI applications that will use your server.
- A Development Environment: You can build MCP servers in Python (using FastMCP) or TypeScript (using the official SDK). Both have excellent documentation and examples.
- A Clear Idea of Your Tools: What capabilities do you want to give your AI? For news aggregation, that means defining which RSS feeds to access and how to structure the data.
The Process
At a high level, building an MCP server follows four steps:
- Define your tools. Each tool has a name, a description (so the AI knows when to use it), and parameters it accepts. For a news server, you might have a “fetch_feed” tool that takes a feed name as input.
- Implement the logic. Write the code that actually performs each tool’s function. This could be fetching RSS feeds, querying databases, calling APIs, or anything else your use case requires.
- Set up the transport. MCP servers typically communicate via “stdio” (standard input/output) for local servers. The SDKs handle this complexity for you—it’s usually just a few lines of configuration.
- Register with your client. Tell your MCP client (Claude Desktop, Cursor, etc.) where to find your server. This is typically a simple configuration file that points to your server’s startup command.
Example in Action
For our news aggregator, the server defines two tools: one that lists available news feeds (Hacker News, TechCrunch, BBC Tech, etc.) and another that fetches articles from a specific feed. The implementation uses standard RSS parsing—nothing exotic.
Once registered with Claude Desktop, you can have conversations like:
“What news feeds do I have available?”
“Fetch the latest from Hacker News and summarise anything about AI.”
“Give me a daily briefing on technology and business trends.”
The AI handles the orchestration—deciding which tools to call, in what order, and how to synthesise the results into a coherent response.
Getting Started
The official MCP documentation at modelcontextprotocol.io provides comprehensive guides and example servers. The Python FastMCP library is particularly beginner-friendly, letting you define tools with simple function decorators. TypeScript developers will find the official SDK equally approachable.
There’s also a growing ecosystem of pre-built MCP servers on GitHub covering everything from database access to Slack integration to file management. These can serve as starting points or inspiration for your own implementations.
The Bigger Picture
MCP represents a fundamental shift in how we think about AI integration. Rather than treating AI as an isolated capability that requires extensive custom engineering to connect with real systems, MCP establishes a universal standard that dramatically lowers the barrier to creating genuinely useful AI-powered workflows.
For businesses, this means faster time-to-value on AI initiatives, reduced integration costs, and access to a growing ecosystem of pre-built capabilities. The network effects are powerful: every new MCP server created benefits every MCP client, and vice versa.
At Teraflow, we’re helping clients leverage MCP to:
- Accelerate their AI automation initiatives
- Build custom integrations connecting AI to their specific business systems
- Develop production-ready MCP servers for enterprise use cases
- Create intelligent workflows that span multiple tools and data sources
The protocol is still young, with adoption accelerating across development tools, IDEs, and enterprise platforms. Now is the time to understand its capabilities and begin exploring how it might transform your own AI initiatives.
Ready to explore what MCP can do for your organisation? Get in touch with Teraflow to discuss your AI enablement journey.
Visit teraflow.ai or reach out to start the conversation.
