What Is MCP (Model Context Protocol) and Why Every Developer Is Talking About It
The Model Context Protocol is the fastest-growing standard in AI tooling. It lets AI models connect to any external system. Here is what it is, how it works, and why it matters for how software gets built.
Admin
Author
What is the Model Context Protocol (MCP)?
The Model Context Protocol (MCP) is an open standard, introduced by Anthropic in November 2024, that defines how AI models communicate with external tools, data sources, and systems.
Before MCP, connecting an AI model to an external system — a database, a Slack workspace, a GitHub repository, a web browser — required custom integration work for every combination of AI model and external tool. A company connecting Claude to their database had to build different integration code than a company connecting GPT-4 to the same database.
MCP solves this with a universal protocol. An MCP server exposes a set of tools following a standardised format. Any AI client that speaks MCP can connect to any MCP server and use those tools — regardless of which AI company built the client or which company built the server.
Why MCP matters: the USB analogy
Before USB (Universal Serial Bus), connecting peripherals to computers was a nightmare. Every device needed its own port, its own driver, its own protocol. A printer manufacturer and a keyboard manufacturer built entirely different connection systems.
USB created a universal standard. Any USB device works with any USB port. The peripheral manufacturer builds to the standard; the computer manufacturer builds to the standard; they connect without custom integration.
MCP is the USB for AI tool integration.
An MCP server built by a database company works with Claude Code, Cursor, Cline, and any other MCP-compatible AI client. The developer writes one integration. Every AI tool that adopts MCP can use it.
How does MCP work technically?
An MCP server is a process that exposes three types of capabilities:
Tools — functions the AI can invoke. A Slack MCP server might expose tools called send_message, list_channels, search_messages. The AI calls these tools during a task; the server executes them and returns results.
Resources — data the AI can read. A filesystem MCP server might expose resources that let the AI read file contents, directory listings, or file metadata. Resources are read-only data access.
Prompts — templated interactions for specific workflows. A code review MCP server might expose a prompt template that structures how the AI approaches a pull request review.
The communication happens over a standardised JSON protocol. The AI client sends requests; the server processes them and returns structured responses.
What MCP servers exist in 2026?
The MCP ecosystem has grown rapidly since Anthropic's initial release. Major categories:
Development tools: GitHub (create PRs, review code, manage issues), GitLab, Jira, Linear, Sentry
Communication: Slack (read channels, send messages, search history), email clients, Microsoft Teams
Data and databases: PostgreSQL, MySQL, SQLite, Supabase, MongoDB — AI can query and update databases directly
Web and browser: Playwright and Puppeteer MCP servers let AI control a browser — navigating, clicking, filling forms, extracting content
Cloud services: AWS, Google Cloud, Vercel, Cloudflare — AI can deploy code, configure services, manage infrastructure
Productivity: Notion, Obsidian, Google Drive, Figma
AI-specific: Perplexity search, vector databases (Pinecone, Weaviate) for RAG (retrieval-augmented generation)
How do you use MCP in practice?
The developer workflow:
- Find or build an MCP server for the external system you want to connect
- Configure your AI client (Claude Code, Cursor, etc.) to connect to the server — typically a JSON configuration with the server's connection details
- The AI now has access to those tools during any conversation in that context
For Claude Code specifically, MCP servers are configured in the Claude Code settings file. Once configured, Claude can invoke any tool the server exposes during a coding session — querying your database to understand data structure, posting a Slack message when a task is complete, creating a GitHub PR when code is ready.
Building your own MCP server
MCP servers can be built in any language with an MCP SDK (Anthropic provides official SDKs for TypeScript/JavaScript and Python). A minimal MCP server:
- Define your tools: name, description, input schema (what parameters it accepts), return schema
- Implement the tool handlers: the actual code that executes when the AI calls the tool
- Connect to the MCP protocol: the SDK handles the communication layer
The philosophy of a good MCP server: expose the minimal set of tools that solve the problem well. An overly broad MCP server (one tool that does anything) is harder for the AI to use effectively than a focused set of well-described specific tools.
Why MCP is more important than it first appears
The long-term significance of MCP is not individual tool connections. It is the emergence of an ecosystem.
When peripherals all used USB, a new peripheral category (e.g., USB-C external drives) could be adopted by every computer immediately. When AI tools all speak MCP, a new MCP server (e.g., one connecting AI to a novel data source) becomes available to every AI tool immediately.
This creates a compounding effect: each new MCP server adds value to every MCP-compatible AI client. Each new MCP-compatible AI client increases the value of every existing MCP server.
We are approximately 18 months into this ecosystem forming. The number of available MCP servers has grown from dozens to thousands. The number of AI clients supporting MCP has grown from one (Claude Code) to most major AI development tools.
The infrastructure for truly agentic AI — AI systems that can act across the full range of digital tools that humans use — is being built on MCP. Understanding the protocol now means understanding the architecture of the AI tooling ecosystem for the next decade.