Introduction to MCP – Why Context Is the New Infrastructure

Large Language Models have shifted software engineering from deterministic logic to probabilistic reasoning. However, as teams move from experiments to production systems, a fundamental limitation becomes clear: models do not manage context, systems do.

Prompt engineering alone does not scale. As applications grow, context becomes fragmented, duplicated, inconsistent, and difficult to govern. The Model Context Protocol (MCP) addresses this problem by introducing a structured, standardized way to expose context, actions, and instructions to language models.

This article introduces MCP from first principles and explains why context should be treated as a first class infrastructure concern, not an afterthought.

What Is MCP?

The Model Context Protocol (MCP) is an open protocol that defines how external systems provide contextual information and executable capabilities to Large Language Models in a structured and discoverable way.

At a high level, MCP allows you to:

• Expose data to a model in a controlled manner
• Expose actions the model can invoke safely
• Define reusable prompts as system level instructions
• Separate model reasoning from system state

MCP does not replace models. It standardizes how models interact with the outside world.

Why Context Management Is Hard in LLM Systems

Modern LLM applications rely on far more than a single user prompt. Real systems require:

• User identity and preferences
• Application state
• Configuration and feature flags
• Historical data
• External system metadata
• Operational constraints

Without structure, this information is often injected directly into prompts. This creates several issues:

• Prompts become long and fragile
• Context duplication increases token usage and cost
• Security boundaries become unclear
• Changes in one part of the system silently break others

Most importantly, models have no concept of ownership or lifecycle of context. Everything is just text.

The Limits of Prompt Only Architectures

Prompt only architectures typically evolve as follows:

1. A short prompt is hardcoded
2. Additional context is appended as text
3. Business logic leaks into prompts
4. Prompts become unmaintainable
5. Teams fear changing anything

This approach does not scale because:

• Prompts are not versioned
• Prompts are not testable
• Prompts mix data, logic, and instructions
• There is no access control over what the model can see or do

MCP addresses these issues by externalizing context and giving it structure.

MCP Core Mental Model

MCP is built around a simple but powerful idea:

To achieve this, MCP defines three core building blocks.

Resources

Resources represent read only contextual data. They answer the question: What information can the model see?

Examples:

• User profile
• Configuration values
• Database records
• CI pipeline metadata

Resources are:

• Structured
• Typed
• Discoverable
• Governed by the system, not the model

Tools

Tools represent actions the model is allowed to perform. They answer the question: What can the model do?

Examples:

• Trigger a deployment
• Create a support ticket
• Query an internal API
• Write data to a database

Tools are:

• Explicit
• Auditable
• Permission controlled
• Executed outside the model

Prompts

Prompts represent reusable instructions. They answer the question: How should the model behave?

Examples:

• System role definitions
• Task specific instructions
• Domain constraints
• Output formatting rules

Prompts are:

• Versionable
• Centralized
• Reusable across models

Context Lifecycle and Ownership

One of MCP’s most important design principles is clear ownership.

• The system owns context
• The model consumes context
• The model never invents state

This separation enables:

• Deterministic behavior
• Safer production systems
• Easier debugging
• Better observability

Context is no longer hidden inside prompts. It is explicit and inspectable.

Where MCP Fits in Modern AI Architectures

MCP acts as an integration layer between models and systems.

Typical architecture:

• Application services manage state
• MCP servers expose resources and tools
• MCP clients broker access for models
• Models focus on reasoning and decision making

MCP complements other patterns such as:

• Retrieval Augmented Generation (RAG)
• Function calling
• Agent frameworks

It does not replace them. It organizes them.

A Minimal MCP Example

Below is a minimal conceptual example of an MCP server exposing a resource.

COPY
from mcp.server import Server

server = Server("example-server")

@server.resource("system_config")
def get_config():
    return {
        "environment": "production",
        "feature_x_enabled": True
    }

server.run()

In this example:

• The model does not fetch configuration itself
• The system decides what is exposed
• The resource can be versioned, audited, and secured

Real World Use Cases

MCP is already proving valuable in areas such as:

• AI assisted DevOps automation
• Data pipeline orchestration
• Internal developer platforms
• Enterprise knowledge systems
• Multi tenant SaaS AI features

Anywhere context matters, MCP adds clarity.

Conclusion

As AI systems mature, context becomes infrastructure.

The Model Context Protocol introduces a disciplined approach to managing that context. By separating data, actions, and instructions from model reasoning, MCP enables scalable, secure, and maintainable AI architectures.

In the next article, we will break down MCP’s core components in depth and explain exactly how resources, tools, and prompts work together in real systems.

Understanding MCP early gives teams a significant advantage as AI moves from experimentation to production.