Introduction
Artificial intelligence has moved beyond simply generating text. Modern AI agents are expected to retrieve information, call APIs, query databases, work with files, execute actions, and interact with business applications.
This creates an important problem: every AI application needs a reliable way to connect with these external systems.
Model Context Protocol (MCP) is designed to solve this problem. MCP is an open standard that provides a common way for AI applications to connect to external data sources, tools, and workflows. Instead of building a separate integration for every AI model and every application, developers can expose capabilities through MCP servers and allow compatible AI clients to use them.
What is MCP?
Think of MCP as a standard communication layer between an AI application and external systems.
For example, suppose you build an AI agent that needs to:
Without MCP, you might build custom integrations for each AI application.
With MCP, these capabilities can be exposed through standardized MCP servers.
A simplified architecture looks like this:
User → AI Application → MCP Client → MCP Server → External System
The MCP architecture separates responsibilities between the host application, clients, and servers.
MCP Architecture
MCP mainly consists of three components:
1. Host
The host is the AI application that the user interacts with.
Examples could include an AI-powered IDE, desktop AI application, or your own AI application.
The host manages MCP clients, permissions, connections, and interaction with the language model.
2. MCP Client
The client acts as the communication layer between the host and an MCP server.
A host can have multiple MCP clients, with each client maintaining a connection to a particular MCP server.
3. MCP Server
The MCP server exposes specific capabilities to the AI application.
For example:
Database MCP Server
↓
Query Database
GitHub MCP Server
↓
Search Repository
File MCP Server
↓
Read Documents
HR MCP Server
↓
Employee Information
MCP servers can be local programs or remote services.
The Three Important MCP Primitives
MCP servers primarily expose three types of capabilities:
Tools
Tools are actions that an AI model can invoke.
For example:
get_employee_details()
search_database()
create_ticket()
send_email()
get_weather()
Tools can interact with APIs, databases, files, and other external systems. MCP defines mechanisms for discovering and invoking these tools.
For example, an AI agent could receive a request:
"Find employee details for Shubham."
The model could determine that it needs an employee lookup tool and call:
get_employee_details(
employee_id="12345"
)
The MCP server then performs the operation and returns the result.
Resources
Resources provide information that an AI application can use as context.
Examples include:
Documents
Database information
API documentation
Git history
Files
Knowledge bases
Resources are generally used to provide information rather than perform an action.
For example:
AI Agent
↓
MCP Resource
↓
Company Policy Document
↓
Relevant information
The AI can then use that information when generating its response.
Prompts
MCP also supports prompts, which are reusable templates or workflows exposed by an MCP server.
For example:
review-code
summarize-document
analyze-invoice
generate-report
A user can select a predefined prompt, and the MCP server can provide the structured instructions required for that workflow.
Why MCP is Important for AI Agents
The biggest advantage of MCP is standardization.
Without a common protocol, developers often need to create custom connectors:
AI Application
├── Custom GitHub integration
├── Custom Database integration
├── Custom SharePoint integration
├── Custom Slack integration
└── Custom API integration
With MCP, the architecture can become:
AI Application
|
MCP Client
|
┌──────────────┼──────────────┐
↓ ↓ ↓
GitHub MCP Database MCP SharePoint MCP
↓ ↓ ↓
GitHub Database SharePoint
This makes integrations more modular and reusable.
MCP is specifically designed around composable servers, allowing different specialized servers to work with the same AI application.
MCP and AI Agents
MCP becomes particularly powerful when combined with AI agents.
Consider an automation agent responsible for processing an invoice.
The agent might need to:
Read the invoice.
Extract invoice information.
Check the supplier in a database.
Verify the purchase order.
Submit the invoice to an accounting API.
Notify the finance team.
An MCP-based architecture could look like:
AI Agent
|
MCP Client
|
┌────────────┼────────────┐
↓ ↓ ↓
Document MCP Database MCP Finance MCP
↓ ↓ ↓
Invoice Supplier DB Accounting API
The AI agent decides which available tools are useful for the task.
This is one of the major differences between a traditional chatbot and an agentic AI system.
A chatbot mainly generates information.
An agent can reason about a task and interact with external systems through tools.
MCP vs Traditional APIs
MCP does not replace APIs.
Instead, MCP can act as a standardized layer that makes existing APIs and systems accessible to AI applications.
For example:
Traditional application:
Frontend → Backend API → Database
With an AI agent:
AI Agent
↓
MCP Tool
↓
Backend API
↓
Database
The underlying API can remain unchanged.
The MCP server provides an AI-friendly interface around that functionality.
MCP in Enterprise Automation
MCP has interesting applications in enterprise automation.
Imagine an organization using:
SharePoint
PostgreSQL
Microsoft Entra ID
Power Automate
UiPath
Dataverse
Internal REST APIs
Document repositories
Instead of creating a completely custom AI integration for every system, MCP servers can expose selected capabilities.
For example:
Enterprise AI Agent
|
MCP Layer
|
┌─────────────────┼─────────────────┐
↓ ↓ ↓
SharePoint MCP Database MCP HR MCP
↓ ↓ ↓
SharePoint PostgreSQL Employee API
The agent could then perform workflows such as:
"Find the employee's department, retrieve the relevant company policy, and create a service request."
The agent could use multiple MCP tools to complete the workflow.
MCP and Security
MCP also introduces important security considerations.
An MCP tool may have permission to:
Therefore, MCP integrations should not automatically trust every tool.
The MCP specification emphasizes user consent, data privacy, access control, and tool safety. Hosts should provide appropriate authorization and control over what information and actions are exposed.
For example, there is a major difference between:
read_customer()
and:
delete_customer()
A production MCP implementation should carefully control which operations an AI agent is allowed to perform.
The Evolution of MCP
MCP is evolving quickly. The July 28, 2026 specification introduced several significant changes, including a more stateless protocol core, multi-round-trip requests, cacheable list results, authorization improvements, and a formal extensions framework.
This shows that MCP is developing beyond a simple "AI tool calling" mechanism toward a broader protocol for connecting AI applications with external capabilities.
A Simple Example
Suppose you create an MCP server for your company's employee system.
It might expose:
Tools:
get_employee()
search_employee()
get_department()
Resources:
employee_policies
organization_structure
Prompts:
employee_summary
employee_report
Now an AI agent could receive:
"Give me the department and current policy information for employee 1024."
The agent could:
1. Call search_employee()
2. Retrieve employee information
3. Access employee_policies
4. Combine the information
5. Generate the answer
The important point is that the AI does not need to know how the underlying database or API works. It interacts with the standardized MCP interface.
MCP's Biggest Advantage
The biggest advantage of MCP is not simply that it lets an AI call a function.
The real value is interoperability.
A developer can build a specialized MCP server once and potentially make its capabilities available to multiple compatible AI applications.
This creates a model similar to what APIs did for traditional software integration:
Traditional world:
API → Applications
AI world:
MCP → AI Applications
MCP therefore has the potential to become an important integration layer for agentic AI systems.
Conclusion
Model Context Protocol provides a standardized way for AI applications to interact with external tools, data, and workflows.
Its core concepts are simple:
Tools → Actions
Resources → Context
Prompts → Reusable workflows
Together, these capabilities allow AI agents to move beyond answering questions and start interacting with real systems.
For developers working with AI agents, automation, APIs, databases, Power Platform, UiPath, Python, or enterprise applications, understanding MCP is increasingly valuable.
The important shift is:
From AI that only generates answers to AI that can securely interact with the systems around it.
MCP provides one of the standardized protocols for making that interaction possible.
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