Understanding Model Context Protocol (MCP) 🔌

MCP are the USB-C of Agentic AI Tool Integrationn

One of the biggest challenges in building AI agents is not the model.

It is integration.

Modern AI applications need access to:

• GitHub repositories
• Slack messages
• Google Drive documents
• PostgreSQL databases
• Jira tickets
• Confluence pages
• Internal enterprise systems

Before MCP, every AI application had to build these integrations independently.

This resulted in enormous duplication of effort across the industry.

To solve this problem, Anthropic introduced the Model Context Protocol (MCP), a standard that is rapidly becoming the universal interface between AI applications and external systems.

Think of MCP as:

USB-C for AI tools and data sources.

Instead of every application building custom integrations, MCP provides a common protocol that allows any AI application to connect to any MCP-compatible service.

The Integration Problem

Consider two AI applications:

AI Research Assistant
AI Coding Assistant

Both need access to:

• GitHub
• Slack
• Google Drive
• PostgreSQL

Without MCP:

Traditional approach:

Every tool must be built manually.

    github_tool()
    slack_tool()
    jira_tool()
    postgres_tool()

This leads to duplicated work across applications.

graph TD

    A[Research Agent]
    --> G1[GitHub API Wrapper]

    A --> S1[Slack API Wrapper]

    A --> D1[Drive API Wrapper]

    B[Coding Agent]
    --> G2[GitHub API Wrapper]

    B --> S2[Slack API Wrapper]

    B --> D2[Drive API Wrapper]

Every team builds the same integrations repeatedly.

If:

• M = number of applications
• N = number of tools

The total integration effort becomes:

$$Work = M \times N$$

This does not scale.

---

The MCP Solution

MCP introduces a standard interface.

Instead of every application integrating with every tool:

    mcp.connect("github")
    mcp.connect("slack")
    mcp.connect("postgres")

The integration complexity shifts to the protocol layer.

graph TD

    A[AI App 1 💡]
    B[AI App 2 💡]
    C[AI App 3 💡]

    A --> MCP[MCP Protocol 🔌]
    B --> MCP
    C --> MCP

    MCP --> G[GitHub Server ⚡]
    MCP --> S[Slack Server ⚡]
    MCP --> D[Google Drive Server ⚡]
    MCP --> P[Postgres Server ⚡]

Now:

$$Work = M + N$$

Applications implement MCP once.

Tool providers implement MCP once.

Everyone benefits.

Why MCP Is Growing Rapidly

MCP solves several problems simultaneously.

1. Standardization

One protocol. Many providers.

2. Reusability

One integration benefits every application.

3. Ecosystem Growth

As more MCP servers become available:

• GitHub MCP
• Slack MCP
• Google Drive MCP
• Jira MCP
• Notion MCP
• Postgres MCP

every AI application gains immediate access.

4. Faster Development

Developers focus on:

• Agent Logic
• Reasoning
• Workflows
• Evaluation

instead of API plumbing.

---

MCP and Agentic AI

MCP fits naturally into agent architectures.

The LLM becomes an orchestrator.

MCP becomes the integration layer.

graph TD

    U[User]

    --> A[Agent 💡]

    A --> G[GitHub MCP 🔌]
    G --> GS[GitHub Server ⚡]
    GS--> G

    A --> S[JIRA MCP 🔌]
    S --> SS[JIRA Server ⚡]
    SS--> S

    A --> P[Postgres MCP 🔌]
    P --> PS[Postgres Server ⚡]
    PS --> P

    G --> A
    S --> A
    P --> A

    A --> R[Response]

 

---

Core MCP Architecture

MCP consists of two primary components:

1. MCP Client 💡

The application consuming resources.

Examples:

• Claude Desktop
• AI Coding Agents
• Research Assistants
• Internal Enterprise Agents

Building an MCP Client

Conceptually, an MCP-enabled agent looks like this:

agent = Agent(
    tools=[
        github_mcp,
        slack_mcp,
        postgres_mcp
    ]
)

The agent no longer cares about:

• authentication details
• API formats
• endpoint structures

It only knows:

"I have access to a GitHub MCP server."

2. MCP Server ⚡

The provider of tools and resources.

Examples:

• GitHub MCP
• Slack MCP
• Google Drive MCP

Architecture:

graph LR

    A[MCP Client 💡]

    A --> B[MCP Server 🔌]

    B --> C[GitHub ⚡]

    B --> D[Slack ⚡]

    B --> E[Database ⚡]

The client asks for data or actions.

The server exposes them through a standardized interface.

---

Resources vs Tools

MCP supports two major categories.

1. Resources 🗞️

Resources are primarily: Read operations

Resources provide context.

Examples:

    README.md
    Slack Messages
    Database Records
    PDF Documents

2. Tools 🧰

Tools are: Executable functions

Tools perform actions.

Examples:

- Create Pull Request
- Post Slack Message
- Create Jira Ticket
- Update Database Record

---

Practical Examples

1. GitHub MCP Server

Suppose a user asks:

Summarize the README.md
from this repository.

The workflow becomes:

Notice that the LLM never directly communicates with GitHub.

Everything flows through the MCP interface.

sequenceDiagram

    participant User
    participant Client
    participant MCP
    participant GitHub

    User->>Client: Summarize README

    Client->>MCP: Fetch README.md

    MCP->>GitHub: Get File

    GitHub-->>MCP: README Content

    MCP-->>Client: File Content

    Client-->>User: Summary

2. Pull Request Analysis

User:

What are the latest pull requests?

The agent dynamically selects a different MCP tool.

graph TD

    A[User Request]

    --> B[List Pull Requests Tool]

    --> C[GitHub MCP Server]

    --> D[Pull Request Data]

    --> E[LLM Summary]

The same server exposes multiple capabilities:

• read files
• list pull requests
• inspect commits
• analyze issues

without the application needing custom integrations.

3. A Real Enterprise Scenario

Imagine an engineering productivity agent.

User asks:

Summarize the open production incidents
and recent pull requests related to them.

The agent may:

1. Query Jira MCP
2. Query GitHub MCP
3. Query Slack MCP
4. Correlate findings
5. Generate summary

Workflow:

graph TD

    A[User Query]

    --> B[Jira MCP 🔌]

    --> C[GitHub MCP 🔌]

    --> D[Slack MCP 🔌]

    B --> E[LLM Analysis]
    C --> E
    D --> E

    E --> F[Summary]

This is exactly the type of cross-system reasoning MCP enables.

---

MCP and the Future of AI Development

The industry is gradually moving from:

Custom Integrations Everywhere

to:

Shared AI Infrastructure

MCP is becoming one of the foundational standards enabling that shift.

Just as:

• HTTP standardized web communication
• SQL standardized database access
• REST standardized APIs

MCP is attempting to standardize:

How AI systems access tools and context.

---

Final Thoughts

Tool use made AI agents practical.

Code execution made them powerful.

MCP makes them scalable.

Without MCP:

$$Work = Applications \times Integrations$$

With MCP:

$$Work = Applications + Integrations$$

That reduction in complexity is why MCP adoption is growing rapidly across the AI ecosystem.

As Agentic AI continues to evolve, MCP is increasingly becoming the connective tissue between LLMs and the software systems they need to interact with.

And for AI engineers, understanding MCP may soon become as important as understanding APIs themselves.