AI Internals

MCP Explained

October 24, 2025 | 6 Minute Read

Introduction

Artificial intelligence models are powerful, but they exist in a vacuum. They have knowledge up to their training cutoff date and can only work with information fed directly into a conversation. What happens when you need an AI to access real-time data, query your company’s database, or control devices in your home lab? This is where the Model Context Protocol (MCP) comes in.

MCP is an open standard that acts as a bridge between AI models and external systems. Think of it as a universal translator that allows AI assistants to securely request data from APIs, databases, file systems, and other services without requiring custom integrations for each one. In this post, we’ll explore what MCP is, how it works, and why it matters for the future of AI applications.

What Is the Model Context Protocol?

At its core, MCP is a standardized specification for how AI models can communicate with external data sources and tools. Instead of building custom code for each integration, developers can create MCP “servers” that expose their tools, databases, or APIs in a way that any MCP-compatible model can understand and use.

The key innovation is standardization. Without MCP, every time you wanted an AI model to access a new service, you’d need to describe that service’s API in detail within your prompt or system instructions. The model would then have to figure out how to call the right endpoints with the right parameters. It’s fragile, prone to errors, and doesn’t scale well.

MCP solves this by defining a clear protocol. An MCP server advertises what it can do—what tools it offers, what parameters they take, and what they return. The AI model receives this information in a structured format, understands it automatically, and can reliably invoke the right operations.

graph LR
    A[AI Model<br/>Assistant] <--> B[MCP Protocol<br/>Standardized Interface]
    B <--> C[MCP Server<br/>Database]
    B <--> D[MCP Server<br/>API]
    B <--> E[MCP Server<br/>File System]
    B <--> F[MCP Server<br/>IoT Devices]

    style A fill:#2ECC40
    style B fill:#FF851B
    style C fill:#0074D9
    style D fill:#0074D9
    style E fill:#0074D9
    style F fill:#0074D9

How MCP Works: The Simple Version

Imagine you have a restaurant reservation database and want an AI assistant to help customers check availability. Here’s the flow:

  1. You build an MCP server that exposes a tool called “check_availability” with parameters for date, time, and party size.

  2. The MCP server defines this in a standard schema that clearly states: “This tool requires a date (string), a time (string), and party_size (number).”

  3. When a user asks the AI “Do you have availability for 4 people tomorrow at 7 PM?”, the AI receives the MCP server’s capability list, understands what tools are available, and calls the check_availability tool with the right parameters.

  4. The MCP server processes the request, queries your database, and returns the result to the AI.

  5. The AI presents the answer to the user: “Yes, we have a table available.”

This entire flow is standardized. The AI doesn’t need custom code or special prompting—it just follows the protocol.

sequenceDiagram
    participant User
    participant AI as AI Assistant
    participant MCP as MCP Server<br/>(Restaurant DB)
    participant DB as Database

    User->>AI: "Table for 4 tomorrow at 7 PM?"
    AI->>MCP: Get available tools
    MCP->>AI: Tools: check_availability(date, time, party_size)
    AI->>MCP: check_availability("2025-10-28", "19:00", 4)
    MCP->>DB: Query availability
    DB->>MCP: Available: Yes
    MCP->>AI: Result: Table available
    AI->>User: "Yes, we have a table available."

    Note over AI,MCP: Standardized MCP Protocol

Real-Life Usage Scenarios

Scenario 1: Corporate Data Access A financial analyst asks their AI assistant, “What was our revenue growth in Q3?” Instead of manually pulling data, the AI calls an MCP server that queries the company’s data warehouse. The result is real, current, and accurate. The analyst gets instant answers without leaving the chat interface.

Scenario 2: Home Automation You have smart lights, thermostats, and security cameras in your home lab. An MCP server exposes these as tools. You can ask your AI assistant, “What’s the current temperature?” or “Turn on the lights in the living room,” and the AI reliably interacts with your devices through the MCP server.

Scenario 3: Knowledge Base Integration A support team uses MCP to connect their AI assistant to the internal knowledge base. When helping customers, the AI can search company documentation, policies, and FAQs in real-time. It provides accurate, consistent answers based on actual company knowledge, not hallucinations.

Scenario 4: DevOps and Monitoring An MCP server exposes system monitoring tools—check CPU usage, disk space, running processes, recent errors. An engineer asks their AI assistant, “What’s wrong with the production server?” The AI queries the monitoring tools, analyzes the data, and provides troubleshooting suggestions.

graph LR
    A[AI<br/>Assistant] --> B[MCP Protocol]

    B --> C1[Enterprise:<br/>Data Warehouses,<br/>Knowledge Bases,<br/>CRM Systems]

    B --> C2[Home & Personal:<br/>Smart Devices,<br/>Files,<br/>Media Libraries]

    B --> C3[Development:<br/>Code Repos,<br/>Build Systems,<br/>Package Managers]

    B --> C4[Operations:<br/>Monitoring,<br/>Logs,<br/>Infrastructure]

    style A fill:#2ECC40
    style B fill:#FF851B
    style C1 fill:#0074D9
    style C2 fill:#0074D9
    style C3 fill:#0074D9
    style C4 fill:#0074D9

Why MCP Matters

Without MCP, integrating external systems with AI requires constant manual effort. You write custom code, describe it to the model in prompts, and hope the model interprets your instructions correctly. This approach is error-prone and doesn’t scale.

MCP enables a future where tools and data sources are AI-ready by default. Developers expose their systems through MCP, and any AI model that understands the protocol can use them reliably. It’s a win for security too—you define exactly what operations are allowed and how they should be executed, rather than trusting an AI to interpret natural language instructions.

Conclusion

The Model Context Protocol represents a fundamental shift in how AI systems interact with the real world. By standardizing the interface between models and external systems, MCP makes AI more practical, reliable, and powerful. Whether you’re building enterprise applications, home automation systems, or experimental tools, MCP provides a framework for safely and reliably connecting your data and services to AI.

As MCP adoption grows, we can expect to see a thriving ecosystem of servers and integrations. For developers, the takeaway is clear: if you have data or tools you want AI systems to access, MCP is the modern way to do it. For users, it means AI models will become increasingly useful as practical assistants that can access real information and perform real actions—not just generate text based on training data.

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