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What MCP Actually Is: A Developer's Perspective

By Jaroslaw Michalik•
MCPEcosystemAI integrations

What MCP Actually Is: A Developer's Perspective

The Foundation: A Protocol, Not Just a Feature

Model Context Protocol (MCP) is fundamentally a communication standard that allows Large Language Models (LLMs) to interact with external systems in real-time. Developed by Anthropic, it's not another AI feature or capability—it's a standardized protocol that enables consistent communication between AI models and external tools, data sources, and services.

Think of MCP as the REST of AI agent interactions. Just as REST standardized how web applications communicate, MCP standardizes how AI agents interact with the digital world around them.

Why MCP Matters for Developers

Accelerated Tool Development

Before MCP, building tools for AI agents meant creating custom integrations for each model and each service. The development process looked like this:

  1. Build a custom connector for a specific LLM
  2. Create proprietary message formats
  3. Handle authentication individually
  4. Repeat for every new service or model

MCP changes this workflow dramatically:

  1. Define your tool's functionality according to the MCP specification
  2. Implement the standardized JSON-RPC 2.0 message format
  3. Connect to any MCP-compatible agent

This standardization reduces development time from weeks to hours, enabling rapid creation of AI-powered tools and integrations.

The REST Analogy: Why It's Spot On

The comparison to REST is particularly apt. Before REST became the standard for web APIs, developers had to learn different integration methods for each service. REST introduced a common language and structure that accelerated web development exponentially.

MCP does the same for AI agent tools by providing:

  • A standard message format (JSON-RPC 2.0)
  • Consistent authentication patterns
  • Uniform tool discovery mechanisms
  • Standardized error handling

The Technical Architecture

MCP implements a client-server architecture with three key components:

  1. Host - The AI application using an LLM (e.g., Claude Desktop, IDE plugins)
  2. Client - Maintains a 1:1 connection with the server, managing bi-directional communication
  3. Server - Provides specialized functions through tools, resources, and prompt templates

The protocol supports two primary transport models:

  • STDIO (Standard Input/Output) for local integrations
  • SSE (Server-Sent Events) using HTTP requests for remote integrations

Practical Implementation

MCP is available through official SDKs in multiple languages:

  • TypeScript
  • Python
  • Java
  • Kotlin
  • C#

The implementation process follows these general steps:

1. Define your tool capabilities
2. Create handlers for each capability
3. Initialize an MCP server
4. Register your tools
5. Start the server

Trying MCP Today

You can experience MCP in action through several clients:

  • Claude Desktop application
  • Zed code editor
  • Replit's AI capabilities
  • Sourcegraph's code intelligence
  • Various IDE plugins and extensions

Integration Possibilities

MCP enables LLMs to seamlessly interact with:

  • Database systems (Postgres, MongoDB)
  • Productivity tools (Google Drive, Slack)
  • Development environments (GitHub, Git)
  • Web browsing and automation (Puppeteer)
  • Custom internal tools and services

From an Experienced Developer's Perspective

As someone who's built integrations both before and after MCP, I can attest that this protocol represents a fundamental shift in AI tool development. The most significant advantages I've experienced:

  1. Context preservation - MCP allows models to maintain conversation context while accessing external tools, eliminating the frustrating "context switching" problems in earlier implementations.

  2. Scalability - The modular design means you can start small and expand tool capabilities incrementally without redesigning your entire system.

  3. Composition power - The real magic happens when combining multiple MCP tools. I've seen development teams build complex workflows by chaining simple MCP tools together, creating capabilities greater than the sum of their parts.

  4. Developer experience - The standardization drastically reduces onboarding time. Developers who understand MCP can quickly work with any compatible tool in the ecosystem.

Looking Forward

As MCP adoption grows, we're likely to see:

  • A thriving ecosystem of interoperable AI tools
  • Open-source MCP implementations for popular services
  • MCP becoming the de facto standard for AI agent interactions
  • Enterprise adoption for internal tool development

For developers looking to build AI-powered tools and applications, learning MCP isn't just a good idea—it's becoming essential knowledge in the rapidly evolving AI landscape.

The most successful AI applications won't be the ones with the largest models, but those that can effectively connect models to the tools and data that make them truly useful. MCP is the bridge making these connections possible.

MCP Directory: Your Resource Hub

In the MCP directory, you'll find a comprehensive collection of resources to help you get started with MCP development. This includes detailed listings of MCP clients, servers, and tools, along with implementation guides and best practices.

Whether you're looking to integrate MCP into your existing applications or build new AI-powered tools from scratch, the directory provides valuable insights and examples to accelerate your development process.