The Model Context Protocol (MCP) is a standardized protocol that connects AI agents to various external tools and data sources.
Imagine it as a USB-C port — but for AI applications.
> Why use MCP instead of traditional APIs?
Connecting an AI system to external tools involves integrating multiple APIs. Each API integration means separate code, documentation, authentication methods, error handling, and maintenance.
> MCP vs API Quick comparison
Key differences Single protocol: MCP acts as a standardized "connector," so integrating one MCP means potential access to multiple tools and services, not just one
Dynamic discovery: MCP allows AI models to dynamically discover and interact with available tools without hard-coded knowledge of each integration
Two-way communication: MCP supports persistent, real-time two-way communication — similar to WebSockets. The AI model can both retrieve information and trigger actions dynamically
> The architecture MCP Hosts: These are applications (like Claude Desktop or AI-driven IDEs) needing access to external data or tools
MCP Clients: They maintain dedicated, one-to-one connections with MCP servers
MCP Servers: Lightweight servers exposing specific functionalities via MCP, connecting to local or remote data sources
> When to use MCP?
- Use case 1 Smart Customer Support System
Using APIs: A company builds a chatbot by integrating APIs for CRM (e.g., Salesforce), ticketing (e.g., Zendesk), and knowledge bases, requiring custom logic for authentication, data retrieval, and response generation.
Using MCP: The AI support assistant seamlessly pulls customer history, checks order status, and suggests resolutions without direct API integrations. It dynamically interacts with CRM, ticketing, and FAQ systems through MCP, reducing complexity and improving responsiveness.
- Use case 2 AI-Powered Personal Finance Manager
Using APIs: A personal finance app integrates multiple APIs for banking, credit cards, investment platforms, and expense tracking, requiring separate authentication and data handling for each.
Using MCP: The AI finance assistant effortlessly aggregates transactions, categorizes spending, tracks investments, and provides financial insights by connecting to all financial services via MCP — no need for custom API logic per institution.
- Use case 3 Autonomous Code Refactoring & Optimization
Using APIs: A developer integrates multiple tools separately — static analysis (e.g., SonarQube), performance profiling (e.g., PySpy), and security scanning (e.g., Snyk). Each requires custom logic for API authentication, data processing, and result aggregation.
Using MCP: An AI-powered coding assistant seamlessly analyzes, refactors, optimizes, and secures code by interacting with all these tools via a unified MCP layer. It dynamically applies best practices, suggests improvements, and ensures compliance without needing manual API integrations.
When are traditional APIs better? Precise control over specific, restricted functionalities
Optimized performance with tightly coupled integrations
High predictability with minimal AI-driven autonomy
MCP is ideal for flexible, context-aware applications but may not suit highly controlled, deterministic use cases.
More can be found here: https://www.youtube.com/watch?v=BwB1Jcw8Z-8