What Is MCP?
The Model Context Protocol (MCP) is an open standard created by Anthropic that defines how AI applications communicate with external data sources and tools. Think of it as a USB-C port for AI — a universal interface that lets any AI tool plug into any compatible service without custom integration code.
Before MCP, every AI coding tool had its own proprietary way of connecting to external services. If you wanted Claude Code to access your database schema, you wrote a custom integration. If you wanted Cursor to read your Jira tickets, you wrote another one. MCP replaces this fragmented landscape with a single, standardized protocol.
The official specification is maintained at modelcontextprotocol.io and the protocol is fully open source under the MIT license.
How MCP Works: The Architecture
MCP follows a client-server architecture with three core components:
MCP Hosts are the AI applications that users interact with — Cursor, Claude Code, Windsurf, and others. The host is what you see on your screen.
MCP Clients are protocol-level connectors that maintain a 1:1 connection between a host and a server. Each client handles the communication lifecycle with one specific server.
MCP Servers are lightweight services that expose specific capabilities. A server might provide access to a database, a file system, an API, or — in Swylink's case — a persistent context memory layer.
The transport layer uses two primary mechanisms:
stdio (Standard Input/Output) is the most common transport for local MCP servers. The host application spawns the server as a child process and communicates via stdin/stdout pipes. This is how most IDE integrations work — when you configure an MCP server in Cursor or Claude Code, the IDE starts the server process and talks to it over stdio. It is fast, secure (no network exposure), and simple to configure.
HTTP with Server-Sent Events (SSE) is used for remote MCP servers. The client sends requests via HTTP POST and receives responses through an SSE stream. This enables cloud-hosted MCP servers that multiple clients can connect to simultaneously.
All messages use JSON-RPC 2.0 as the wire format. A typical MCP interaction looks like this:
- The host starts the MCP server process (stdio) or connects to the remote endpoint (HTTP)
- Client and server exchange capability negotiations
- The server advertises its available tools, resources, and prompts
- The AI model decides when to call server tools based on user requests
- Results flow back through the client to the host
The Three Primitives
MCP servers expose capabilities through three primitive types:
Tools are functions the AI can call. For example, a context memory server might expose save_context and search_context tools. The AI model decides when to invoke these based on the conversation. Tools are the most powerful primitive because they allow the AI to take action.
Resources are data the AI can read. These are similar to GET endpoints in a REST API — they provide information but do not change state. A file system server might expose project files as resources. Resources use URI-based addressing (e.g., file:///path/to/code).
Prompts are reusable templates that guide AI behavior. A server can provide prompt templates that help the AI use its tools effectively. For example, a context server might include a prompt template that instructs the AI to save context after every significant decision.
Which IDEs Support MCP?
MCP adoption has been rapid across the AI coding tool ecosystem. As of early 2026, these major tools support MCP:
Cursor was one of the earliest adopters. MCP servers are configured in .cursor/mcp.json at the project root. Cursor supports both stdio and SSE transports and allows per-project MCP configurations.
Claude Code supports MCP natively as part of its architecture. Configuration happens through project settings, and Claude Code can run multiple MCP servers simultaneously.
Windsurf (Codeium) added MCP support in early 2025. Configuration lives in the Windsurf settings and supports stdio transport for local servers.
GitHub Copilot added MCP support in VS Code, enabling Copilot Chat to use MCP server tools during conversations. This brought MCP to the largest user base of AI coding tools.
OpenAI Codex integrated MCP support for its CLI-based coding agent, allowing it to interact with external services during autonomous coding sessions.
Cline and Roo Code are VS Code extensions that support MCP as a core feature, with easy server configuration through the extension settings panel.
Google Gemini added MCP support in its AI Studio and coding integrations, bringing the protocol to Google's ecosystem.
This breadth of adoption means that any service built on MCP — like a persistent context layer — automatically works with every major AI coding tool.
Why MCP Matters for Developers
MCP solves three critical problems in the AI tooling ecosystem:
Interoperability: Before MCP, switching AI tools meant losing all your integrations. If you built a custom connection between Cursor and your project management tool, that connection did not work in Claude Code. MCP means you build one server and it works everywhere.
Composability: MCP servers are modular. You can run a database server, a context server, and a documentation server simultaneously. The AI model orchestrates between them based on what it needs.
Security: MCP includes built-in security primitives. Servers declare their required permissions upfront. The host application controls which servers can access what. Sensitive operations require explicit user approval. This is far more secure than giving AI tools raw API keys to external services.
Building and Using MCP Servers
If you want to build your own MCP server, the ecosystem provides SDKs in multiple languages: TypeScript, Python, Java, Kotlin, C#, and Go. The TypeScript SDK is the most mature and widely used.
A minimal MCP server in TypeScript looks roughly like this: you create a server instance, register your tools with their input schemas, implement the tool handlers, and start the server on stdio transport. The MCP SDK handles all the JSON-RPC communication, capability negotiation, and error handling.
For most developers, though, the value of MCP is in using existing servers rather than building new ones. The MCP ecosystem already includes servers for databases (PostgreSQL, MySQL, SQLite), file systems, Git repositories, cloud services (AWS, GCP), project management tools (Linear, Jira), and specialized services like persistent context layers.
MCP and the Future of AI Tooling
MCP represents a fundamental shift in how AI coding tools work. Instead of monolithic applications that try to do everything internally, the future is composable AI systems where specialized MCP servers handle specific capabilities and the AI orchestrates between them.
This composability is especially important for context persistence. A universal context layer — connected via MCP to every AI tool in your stack — means that any decision you make in any tool becomes available to every other tool. The protocol makes this architecturally possible; services like Swylink make it practical.
The MCP specification continues to evolve. Recent additions include streamable HTTP transport (replacing SSE), better authentication flows for remote servers, and improved tool annotation for helping AI models understand when and how to use tools. The protocol is building toward a future where AI tools are as interchangeable as USB devices — plug in any tool, and it just works with your existing setup.