What Is MCP (Model Context Protocol)?

MCP (Model Context Protocol) is an open protocol from Anthropic that lets AI models connect to external tools and data sources. By default, models are isolated — they only see what you type. MCP gives them "hands": the ability to read files, query databases, call APIs, and use other tools. It is a standard way for AI applications to extend their capabilities.

If you use Claude in Cursor or another MCP-enabled client, you have already seen it in action. The model can browse your codebase, run commands, or fetch data because MCP servers expose those capabilities as tools the model can call.

The Problem MCP Solves

Models are powerful at reasoning and generating text, but they lack direct access to:

Your file system
Databases
APIs and web services
Real-time information

Before MCP, every application built its own integration layer. There was no common way to say "this model can use these tools." MCP defines that interface: servers expose tools, clients (AI applications) connect to servers and let the model invoke them.

How MCP Works

Servers — Software that exposes tools and resources. A filesystem server might expose "read_file" and "list_directory." A database server might expose "query." Servers run locally or remotely and communicate over the protocol.

Clients — AI applications (Claude desktop, Cursor, etc.) that connect to MCP servers and pass tool calls to the model. The model decides when to call a tool; the client executes the call and returns the result.

Tools — Named actions the model can invoke. Each tool has a name, description, and parameters. The model sees the tool list and chooses which to call based on the user's request.

Resources — Read-only data the model can access (e.g., document contents, configuration). Different from tools, which perform actions.

Real-World Examples

Filesystem — Model reads and writes files in a project directory
Database — Model runs queries against Postgres, SQLite, or other DBs
Web — Model fetches URLs, scrapes pages, or calls REST APIs
Git — Model checks status, diffs, and branch info
Slack/Discord — Model sends messages or reads channels

Developers and power users run multiple MCP servers so a single model session can access many tools.

MCP vs. Function Calling vs. Plugins

Function calling — A general term for models invoking external functions. OpenAI, Anthropic, and others support it in their APIs. MCP is one implementation: it standardizes how those functions are defined and discovered.

Plugins — App-specific extensions (e.g., ChatGPT plugins). Each platform has its own plugin system. MCP is platform-agnostic: the same server can work with any MCP-compatible client.

MCP does not replace function calling; it provides a portable, open way to define and expose tools that any client can use.

The Ecosystem in 2026

MCP adoption is growing. Anthropic's Claude desktop and Cursor support it natively. Other IDEs and AI tools are adding support. The protocol is open, so anyone can build servers or clients. Popular servers cover filesystems, databases, web search, and productivity apps. The >MCP server registry lists many options.

Why It Matters for Stack Building

Tools with MCP support integrate more easily. A model that can call your database, read your docs, or trigger workflows is more useful than one that cannot. When evaluating AI tools, check whether they support MCP or similar tool-calling standards — it affects how well they fit into a connected stack.

How This Connects to Hokai

The >Model Directory notes which tools support MCP or tool use. When you run >Smart Match for integration-heavy use cases, recommendations may favor tools with strong tool-calling support. If you need AI that can act on your data and systems, MCP compatibility is a useful filter.

The Bottom Line

MCP gives AI models a standard way to connect to external tools and data. Servers expose tools; clients let models call them. It is becoming the default for extending model capabilities, and tools that support it integrate better into your stack.