Connecting MCP Servers With Claude Code

Claude Code can integrate with hundreds of external tools and data sources through the open-source Model Context Protocol (MCP). MCP is an open standard introduced to bridge AI assistants with data and tools. By adding MCP “servers” (connectors) to Claude Code, we empower the assistant to interact with our apps, databases, APIs, and services in a standardized way. This is achieved without writing custom integration code for each tool.

Why does this matter? MCP is like what browser plugins did for the web, but for AI. It adds new features in a flexible, modular way that works across different models and systems. It frees AI from being ‘trapped’ in training data so it can access real-time context and perform actions in external systems. Instead of using fragile scripts or proprietary plugins, MCP offers a unified protocol supported by a growing network of servers.

In this lesson, we cover how to connect Claude Code to external tools via MCP, focusing on practical how-tos and examples. We will walk through installing and managing MCP servers and then explore two hands-on examples: using Playwright to give Claude the ability to control a web browser and using Hugging Face to let Claude search AI models and datasets. We close with a brief look at other popular MCP servers and best practices.

What is Model Context Protocol (MCP)?

MCP stands for Model Context Protocol, an open standard for integrating AI assistants with external context and tools. In simpler terms, MCP defines a way for an AI (like Claude) to call tools and functions in a structured manner, standardized across platforms.

Each MCP “server” exposes tools, resources, or actions that the AI, in this case Claude Code, can use. For example, one server might expose a tool to query a database, another a tool to fetch the latest Jira ticket details, and another a resource that lists Figma design files. The motivation is to solve the “context integration” problem: allow an AI to pull in the right data at the right time, and perform real actions such as updating documents or sending emails. Before MCP, integrations were often ad hoc, which made them difficult to scale. MCP streamlines this with a uniform API: developers can implement an MCP server for a service, and any AI client that supports MCP can connect to it.

How does it work? MCP uses a request and response schema in which the AI client (Claude Code in our case) communicates with the MCP server over a supported transport such as HTTP, SSE (Server-Sent Events), or STDIO (standard input and output). The server advertises available tools and resources (each with a name, description, and JSON schema for parameters). When we ask Claude to use a tool (for example, “open a browser to example.com”), Claude selects the Playwright tool, formulates a function call through MCP, and the server executes it. This happens behind the scenes; to you, ...