What is the Model Context Protocol (MCP)?

Before MCP, every AI app implemented tool integrations bespoke. Cursor had its own way of exposing your codebase to Claude. Claude Desktop had its own file-system and Slack connectors. Every new app meant reimplementing the same patterns. MCP standardizes the interface so an integration built once works everywhere. The architecture has three parts. (1) MCP Servers: small programs (written in any language) that expose tools ('call this function'), resources ('read this data'), and prompts ('use this template'). Servers run locally (via stdio) or remotely (via HTTP/SSE). (2) MCP Clients: LLM applications — Claude Desktop, Claude Code, Cursor, Zed, Windsurf, and many others — that discover servers, load their capabilities, and expose them to the underlying LLM. (3) The protocol itself: a JSON-RPC 2.0 spec with defined methods for initialization, tool discovery, tool invocation, resource reading, and progress notifications. In practice, this means: you install an MCP server for Postgres once. Now Claude Desktop can query your database. Cursor can query your database. Any future MCP client can query your database. Compare to the pre-MCP world where each app needed its own Postgres plugin. Anthropic open-sourced reference servers for filesystem, GitHub, Slack, Postgres, Puppeteer, Brave Search, and more when MCP launched; the community ecosystem in 2025-2026 has produced hundreds of servers covering most common SaaS. Technically MCP sits a layer above function calling. Function calling is how one LLM call invokes a specific tool. MCP is how tools get discovered, authenticated, and registered with any LLM app at all. An MCP client translates MCP tool definitions into native function-calling schemas for the underlying model (Claude, GPT, Gemini — MCP is model-agnostic). An MCP server doesn't care which LLM is on the other side. Key primitives. (1) Tools: invocable functions with JSON schemas, exactly like function calling, but with standard error handling, progress reporting, and cancellation. (2) Resources: readable data identified by URIs (file:///, github://, postgres://) — lets the client pull context without calling a tool. (3) Prompts: reusable prompt templates a server offers, so an admin can define 'standard SQL review prompt' once and every MCP client can use it. (4) Sampling: a server can ask the client's LLM to sample text — useful for server-side agentic behavior. Adoption trajectory: Anthropic shipped MCP in late 2024 with Claude Desktop as the reference client. Claude Code, Cursor, Windsurf, Zed, and many indie apps added client support through 2025. By early 2026, MCP is on track to become the de facto standard for LLM-tool integration, analogous to how LSP (Language Server Protocol) became the standard for editor-language server communication. OpenAI has not formally adopted MCP but its Assistants API concepts are similar; Google Gemini supports MCP-style tool integration. Tycoon exposes its AI employees through MCP-compatible tool servers so external Claude-based workflows can invoke them, and consumes MCP servers internally to extend its AI employees' capabilities — e.g., an AI CTO can use any Postgres, GitHub, or Linear MCP server you have configured.