MCP Explained in 5 Minutes
A concise, practical guide to what Model Context Protocol (MCP) is, how it connects AI applications to tools and data, and step-by-step examples using Claude Code with Tavily, GitHub, and Playwright.
Useful takeaways from this story.
MCP standardizes how an AI host discovers and calls external tools, resources, and prompt templates through a client-server interface.
Claude Code acts as a host with built-in MCP client commands (for example, claude mcp list and /mcp) to manage MCP servers such as Tavily and GitHub.
Three practical MCP setups: Tavily for live web search and crawling, GitHub for repository workflows via a PAT, and Playwright for browser automation.
# What MCP is
MCP (Model Context Protocol) is a standardized way for AI applications to connect with external tools and data sources. It does not replace underlying APIs. Instead, an MCP server exposes capabilities — tools (actions), resources (readable data), and prompts (reusable templates) — through a uniform interface that an AI host can discover and call.
# How MCP works, simply
- Host: the AI application (for example, Claude Code).
- MCP client: runs inside the host and connects to one or more MCP servers.
- MCP server: exposes tools, resources, or prompts and proxies calls to underlying services or APIs.
In practice, the model decides when it needs external information or an action. The model inspects available MCP tools, selects an appropriate one, and sends the request through the MCP server. MCP handles discovery, transport, and the server-side integration with the real service, while the model handles the reasoning and next steps.
# Practical setup in Claude Code
Claude Code includes commands and an interactive /mcp panel to manage MCP servers. Two commands shown in the guide:
- claude mcp list — shows servers configured on the machine.
- claude mcp add — registers a new MCP server with optional transport and headers.
The guide walks through three useful MCP servers and provides concrete commands.
# Example MCP servers and use cases
- Purpose: give models live web search, extraction, crawling, and research tools.
- Add command: claude mcp add --transport http tavily https://mcp.tavily.com/mcp
- Usage: open Claude Code, run /mcp, select Tavily, complete auth if required, then ask the model to search for current releases or recent news. Claude discovers Tavily's web-search tool and uses it without needing to know Tavily's API details.
- Add command (uses a Personal Access Token):
claude mcp add --transport http github https://api.githubcopilot.com/mcp/ \ --header "Authorization: Bearer YOUR_GITHUB_PAT"
- Security note: replace YOUR_GITHUB_PAT with a PAT that has only the permissions you actually need.
- Purpose: enable browser-based interactions like automated navigation, scraping, or UI testing.
# Typical flow for a task
- Model determines a task requires external data or an action.
- Model inspects the MCP client's discovered tools and resources.
- Model selects the appropriate tool and sends a request via the MCP client to the MCP server.
- MCP server performs the action against the underlying API or service and returns results.
- Model integrates the results into its reasoning and completes the task.
# When to use MCP
Use MCP when you want a single, consistent interface for multiple external services so the model can discover and call tools without bespoke integrations. It's useful for live web research, repository automation, and browser-driven workflows.
# Practical caveats
MCP standardizes discovery and access, but it does not replace good authentication practices or permission scoping. Configure tokens and headers with least privilege and complete any server-specific authentication flows described in the host UI.
# Final point
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Explaining Floor
For the general public. These videos were used at European Championships 2026. Click PLAY or watch it on YouTube.
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