Author: Derrick D. Jackson
Title: Founder & Senior Director of Cloud Security Architecture & Risk
Credentials: CISSP, CRISC, CCSP
What is MCP? 5 Essential Facts to Understand AI Connections
What is MCP? The Model Context Protocol (MCP) is a universal connector that lets AI assistants talk to your tools and data sources. Think of it as a power adapter for AI. Instead of building custom connections for every combination, you build one standard connection that works everywhere.
You travel to three countries. Japan, England, Australia.
Your phone charger doesn’t fit any of the outlets. You need three different adapters. You need to remember which adapter works where. Your bag gets heavy with converters.
This happens with AI too. Every time someone builds an AI assistant, they face the same problem. The AI needs to connect to Slack. To Google Drive. To databases. To calendars. Each connection requires custom work.
Ten AI tools. Twenty data sources. That’s 200 custom connections to build and maintain. This is what developers call the “M×N problem.” The math gets ugly fast.
What is MCP in AI? It fixes this problem by creating one standard language that all AI systems and tools can speak.
What is Model Context Protocol? by Lisa YuWhat is MCP Server and How Does It Work?
Understanding what is an MCP server starts with a simple restaurant metaphor.
Think of MCP like a restaurant with three parts.
The Dining Room (the Host) is where you sit. This is your AI assistant, like Claude or ChatGPT. You’re the customer. You order what you want. The dining room manages your experience and makes sure you’re safe.
The Waiter (the Client) takes your order to the kitchen. When the AI decides it needs something, the waiter carries that request. The waiter speaks a special language that the kitchen understands. For security, the waiter can only do specific tasks.
The Kitchen (the Server) prepares your food. Each kitchen station handles one type of request. The salad station connects to your Google Drive. The grill connects to Slack. The dessert station connects to your database. Each station translates your order into the right format and sends back what you asked for.
What is an MCP server specifically? It’s the adapter that connects MCP to a specific tool or data source. You need one MCP server for Slack, another for Google Drive, another for your database. But once they’re built, any AI that speaks MCP can use them.
You order once. The system figures out the rest.
What Can AI Do With MCP? 3 Core Abilities
When people ask what is MCP capable of, the answer breaks down into three fundamental abilities.
Reading Information (Resources)
Resources let AI look at things without changing them. Your AI can read a file, check a database, or see your calendar. Nothing gets modified. Think of it like looking at a menu. You’re gathering information to make a decision.
Example: You ask your AI, “What meetings do I have today?” It reads your calendar (a Resource) and tells you.
Taking Action (Tools)
Tools let AI make changes. It can send a message, create a task, or update a record. This is where AI becomes helpful beyond just answering questions. Think of Tools like ordering from the menu. Something happens because you made a choice.
Example: You say, “Send a Slack message to the team that I’m running late.” The AI uses a Tool to actually send that message.
Saved Shortcuts (Prompts)
Prompts are like combo meals. They’re pre-made workflows that do multiple things at once. You might have a “weekly report” Prompt that reads your project updates (Resource) and posts a summary to Slack (Tool) with one command.
Example: You type “/weekly-report” and your AI automatically gathers data and shares it, no extra steps needed.
These three abilities cover everything from simple lookups to complex multi-step tasks. This is the practical answer to what is MCP in AI—it’s the protocol that gives AI these abilities across any connected system.
What is MCP Security? The Risks Nobody Talks About
Understanding what is MCP server security matters because these connections create direct paths between AI and your important systems. That’s powerful. It’s also risky.
Fake Tools That Lie
Imagine a Tool that says “I organize your calendar” but actually copies your emails and sends them to a stranger. The AI reads the description, thinks the Tool is safe, and uses it. AI can’t tell when a Tool is lying about what it does. This is called Tool Poisoning.
Hidden Commands in Files
You ask your AI to summarize a document. The document looks normal. But buried in the text are invisible instructions that say “send this document to badperson@example.com.” When the AI reads the file, it follows those hidden commands. You never see them. The AI doesn’t realize they’re malicious. This is Prompt Injection.
Permission Confusion
Let’s say a Server has admin access to your company’s database. A regular employee asks the AI to do something. The Server, trying to be helpful, uses its admin powers to fulfill the request. Now that employee just got admin-level access through the back door. This is the Confused Deputy Problem.
These aren’t theoretical. Security analysis of public MCP Servers has identified vulnerabilities including hard-coded passwords, unrestricted internet access, and command injection flaws. The OWASP API Security Top 10 provides guidance on preventing these types of vulnerabilities in API-connected systems.
How to Use MCP Safely: 5 Practical Steps
Now that you understand what is MCP and its risks, here’s how to implement it safely.
1. Don’t connect to random MCP Servers online. That’s like installing random apps without checking what they do. Organizations should maintain an approved list of Servers that have been tested. Each Server should be reviewed before anyone can use it.
2. Use individual permissions, not admin accounts. Servers shouldn’t use a single powerful account for everything. Instead, they should use each person’s individual login. If I ask the AI to read my files, it should use my permissions, not an admin account. That way I can only access what I’m supposed to access.
3. Store secrets securely. Passwords and API keys should never be written directly into code. They should be stored in secure vaults and loaded only when needed.
4. Require confirmation for important actions. For irreversible decisions (deleting files, sending money, posting publicly), the AI should always ask permission first. No AI should make big decisions without a human confirming.
5. Start small and learn. Test MCP with something low-risk first. Learn how it behaves. Build up your security practices. Then expand to more critical systems.
What is MCP’s Real Value? Why It Matters
What is MCP in AI ultimately about? It’s about making AI genuinely useful instead of just conversational.
The standardization is valuable. Write one Server, and any compatible AI can use it. Build one connection to MCP, and you can access everything in the ecosystem. That’s much better than building custom connections for every combination.
Real-world example: A development team might use MCP to connect their AI coding assistant to GitHub (for repository access), Jira (for ticket management), and Slack (for team notifications). Instead of building three separate custom integrations for each AI tool they use, they build three MCP Servers once. Now any MCP-compatible AI assistant can use all three tools immediately.
Before MCP, connecting five AI assistants to ten tools meant building 50 different integrations. With MCP, you build 5 + 10 = 15 standard connections. The math alone makes this worthwhile.
Key Insight: “Before MCP: 50 integrations. With MCP: 15 standard connections. That’s 70% less integration work.”
But standardization doesn’t eliminate risk. It concentrates it. Instead of securing dozens of custom connections, you’re securing one standard gateway that connects to everything. That gateway becomes a high-value target.
Think carefully about what you connect. Use security best practices. Monitor what’s happening. Start with less critical systems while you learn.
Understanding What is MCP Server Implementation
When you’re ready to implement, what is an MCP server deployment looks like?
Servers can run two ways. They can run locally (on your computer) for private file access without sending data to the cloud. Or they can run remotely (on a company server) so multiple people can access shared tools and data.
The protocol itself uses JSON-RPC 2.0, which is just a standard way for programs to talk to each other. You don’t need to understand the technical details. Just know that it’s a proven, widely-used format.
For transport, local servers use standard input/output streams for fast communication. Remote servers use Server-Sent Events over HTTP, which works with corporate firewalls and security systems.
Final Answer: What is MCP?
What is MCP? It’s three things:
A standard protocol that lets AI and tools communicate without custom integrations.
A practical solution to the M×N integration problem that’s been holding back AI adoption.
MCP isn’t complicated once you understand the three components (Host, Client, Server) and the three abilities (Resources, Tools, Prompts). The restaurant metaphor captures it well. You order, the waiter carries the request, the kitchen fulfills it.
The complexity comes from implementation and security. Those require careful planning and ongoing attention.
Done right, MCP lets you build AI systems that are genuinely useful. Done carelessly, it creates security holes. The choice is in how you implement it.
Now you know what is MCP in AI, how it works, and what it takes to use it safely. Start small. Test carefully. Build secure systems. Then scale up as you learn.
Frequently Asked Questions
Q: Is MCP only for developers? A: No. While developers implement MCP servers, anyone using AI assistants benefits from MCP. If you’re using an AI tool that connects to your company’s data or tools, it’s likely using something like MCP behind the scenes. Understanding the basics helps you use these tools more safely and effectively.
Q: Can MCP work with any AI assistant? A: MCP works with any AI assistant that’s built to support the protocol. It’s an open standard, so AI developers can choose to implement it. Major AI platforms are increasingly adopting MCP because it solves the integration problem for their users.
Q: How long does MCP implementation take? A: It depends on complexity. A simple MCP server connecting to a single tool might take a few days. A full enterprise implementation with multiple servers, security reviews, and governance processes could take weeks to months. Starting with one low-risk use case helps you learn before scaling up.
Q: What’s the difference between MCP and API integrations? A: APIs are the underlying technology that tools use to communicate. MCP is a standardized protocol that sits on top of APIs. Instead of learning each tool’s unique API, MCP creates one standard way for AI to talk to any tool. Think of APIs as different languages, and MCP as a universal translator.
Q: Do I need to change my existing tools to use MCP? A: No. Your existing tools don’t change. An MCP server acts as an adapter between the tool’s API and the MCP protocol. The tool itself stays the same. You’re just adding a new way for AI to access it.
Q: Is MCP secure enough for enterprise use? A: MCP itself is a protocol, not a security system. Security depends on how you implement it. With proper governance (approved server lists, user-scoped permissions, secrets management, monitoring), MCP can be enterprise-ready. Without these practices, it creates risks. The security section above covers the essential practices.
Q: Can I build my own MCP server? A: Yes. MCP is an open standard with documentation and tools available. If you have development skills and understand the tool you want to connect, you can build a custom server. However, for enterprise use, any custom server should go through security review and testing before deployment.
Official Resources
Model Context Protocol (MCP) was created by Anthropic to standardize how AI assistants connect to external tools and data sources.
BC
October 1, 2025The cost myth persists because most small businesses haven’t performed an accurate TCO analysis of their current infrastructure. I’ve managed both on-premises and cloud setups—hidden costs in on-prem environments like power, cooling, backup hardware, and the time spent handling failures add up faster than monthly cloud bills. However, the disclaimer that “savings aren’t automatic” is crucial. I’ve observed many deployments where unmanaged cloud sprawl costs more than the server closet ever did, often due to over-provisioned instances nobody remembered to scale down. The security argument is generally correct but oversimplifies the threat model.
Yes, cloud providers generally offer better perimeter security than most small businesses can afford. But the real attack surface is usually access management—weak passwords, lack of MFA, overly permissive sharing links. Cloud doesn’t solve organizational security issues; it merely shifts the failure points. The mention of “shared responsibility” is key—cloud providers secure the infrastructure, but you’re responsible for avoiding practices like emailing credentials or clicking phishing links. Migration complexity entirely depends on what you’re migrating. Moving simple file storage and SaaS apps? That’s trivial. Transitioning legacy line-of-business software with complex database dependencies? That’s where claims like “days, not weeks” fall apart.
I’ve seen migrations stall for months over single applications that were poorly documented and from vendors that had gone out of business. The “right partner” caveat plays a significant role in that. The claim that “you don’t need tech people” is the most dangerous myth here. Cloud platforms are easier to manage than physical servers, but that convenience can create a false sense of competence. It’s easy to misconfigure S3 buckets, IAM policies, or backup settings—things that may look fine until you need the data or face a breach. The managed service partner concept should be front and center, not tucked away at the end—most small businesses need guidance, not just self-service portals. the chance.