MCP is a standardized protocol that gives AI applications a consistent way to discover and use external tools, resources, and prompts, instead of requiring every AI application to build a separate custom integration for every system
Non-Techincal Explaination
MCP is just a shared toolbox a standard collection of tools and APIs that any AI model can reach into, instead of every developer building their own private toolbox from scratch.
| Aspect | Traditional Integration | MCP-Based Integration |
|---|---|---|
| Integration setup | Straightforward for individual APIs, with each LLM integrating directly through its own wrapper | Simplifies connectivity by allowing the host to connect directly to an MCP server |
| Ownership & maintenance | Each engineering team builds, manages, and updates its own integration, leading to duplicated effort at scale | Integration logic is maintained by the server owner, typically the API or service provider |
| Handling API changes | Any change to the underlying provider API requires each individual integration to be modified | The MCP server handles provider-side changes, allowing connected clients to remain unchanged |
| Authentication & security | Authentication is implemented and managed separately for each tool or integration | Authentication is still required, but responsibilities can be consolidated and managed centrally at the server layer |
Before MCP
After MCP
-
Imagine you have an AI assistant that needs to work with:
- GitHub
- Google Drive
- Slack
- A database
- Company's internal APIs
-
Without MCP, the AI application has to build a special integration for every service.
AI ├── Custom GitHub integration ├── Custom Slack integration ├── Custom Jira integration ├── Custom Database integration └── Custom Google Drive integration -
And every integration may work differently,The developer has to figure out:
- How do I tell GitHub what I want?
- How do I authenticate with Slack?
- How do I retrieve data from this database?
MCP introduces a common protocol for AI applications to communicate with tools and data.
flowchart LR
U[👤 User] --> AI[🤖 AI Model]
AI --> MCP[MCP Client]
MCP --> JR[Jira]
MCP --> SL[Slack]
MCP --> DB[Database]
AI --> U
- The AI doesn't need to understand every tool's unique integration mechanism.
- The MCP server exposes capabilities in a standardized way.
- MCP is like a standard USB interface for AI applications to connect with external tools and data.
There are typically three important pieces:
flowchart LR
A["AI Application<br/>/ MCP Host"]
B["MCP Server<br/><br/>Tools<br/>Resources<br/>Prompts"]
C["External System<br/><br/>GitHub / DB /<br/>Slack / etc."]
A -->|MCP| B
B --> C
- The MCP server acts as the standardized bridge.
- The important idea is that the AI application doesn't have to implement the entire Tools specific interaction itself.
- Suppose you're building an AI coding assistant.
- User asks: "Check GitHub issues and create a Jira ticket for the important ones"
- Without MCP:
AI Application ┌─────────┴─────────┐ ↓ ↓ GitHub integration Jira integration ↓ ↓ GitHub API Jira API - Now imagine adding:
- Slack
- Database
- Google Drive and many more.
- You end up with a growing collection of custom integrations.
- The AI application has a standard way of interacting with MCP servers.
- Each MCP server handles the specifics of its underlying system.
AI Application
│
↓ MCP
┌──────────────────┐
│ MCP Servers │
└────────┬─────────┘
┌────────────┼────────────┐
↓ ↓ ↓
GitHub MCP Jira MCP Slack MCP
↓ ↓ ↓
GitHub Jira Slack
MCP is a standard protocol for exposing tools, resources, and prompts to AI applications
flowchart LR
A["AI<br/><br/>What should I do?"]
B["MCP Server<br/><br/>Here are the capabilities<br/>you can use."]
C["External System<br/><br/>GitHub / Jira / DB / Slack"]
A -->|MCP| B
B -->|API / SDK / DB driver| C