MCP — the Model Context Protocol — is an open standard introduced by Anthropic in late 2024 for connecting tools, data sources and resources to LLM applications. It defines a wire format and a server/client architecture so that any MCP-compatible LLM client (Claude Desktop, Cursor, Zed, Continue, custom apps) can talk to any MCP server (your local filesystem, your database, your internal API, GitHub, Slack, hundreds of community-built integrations).
The architecture:
- MCP server — a process that exposes tools, resources and prompt templates over a standardised protocol. Servers are typically small Python or TypeScript programs.
- MCP client — the LLM-powered application that consumes those servers. Claude Desktop, Cursor and many others ship as clients.
- Tools — functions the LLM can call (search files, query database, call API).
- Resources — read-only context the LLM can fetch (file contents, documentation, structured data).
- Prompts — reusable prompt templates that servers can offer to clients.
Why MCP took off through 2025–2026:
- Standardisation killed bespoke integration code — every app no longer needs to write a custom GitHub integration, a custom Slack integration, a custom database integration. One MCP server works everywhere.
- Local-first by default — most MCP servers run on your own machine with your own credentials, so connecting an LLM to your private data does not require uploading anything.
- Vendor neutral — the spec is open; OpenAI, Google and others have adopted compatibility, making MCP genuinely cross-provider.
- Massive community — hundreds of public MCP servers for everything from Linear and Notion to Postgres and Kubernetes.
Common 2026 use cases:
- Coding workflows — Cursor or Claude Code connects to MCP servers for filesystem, git, database, browser automation and your internal APIs.
- Knowledge worker agents — Claude Desktop with MCP servers for Google Drive, Notion, Linear and Slack reads across your workspaces.
- Internal AI platforms — companies build private MCP servers exposing their own tools and let any compatible LLM client use them safely.
- Research and analysis — connect to data warehouses, BI tools and analytics APIs through standardised servers.
The practical advice for a US developer in 2026: if you find yourself writing tool-calling glue code for an LLM, check whether an MCP server already exists. The community-built ecosystem now covers the majority of common SaaS APIs and developer tools, and the official Anthropic registry plus community lists make discovery straightforward. For internal company tools, building an MCP server is a few hundred lines of code and immediately makes them usable from any compatible LLM client without further integration work.
