Why MCP
Custom tool glue does not scale. MCP gives every AI app the same way to discover tools, read data, and run actions.
Before USB-C you carried a bag of chargers. MCP is one cable that fits the laptop, the phone, and the agent.
Visual Workflows
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Overview
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The one-sentence flow
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Host app → MCP client → MCP server → real system (files, GitHub, database).
Key Takeaways
- 1.MCP is a shared USB-C port for AI tools — one protocol, many apps. Before MCP, every agent rewrote Slack, GitHub, and database wiring.
- 2.Build one MCP server and Cursor, Claude, and your agent can all use it. MCP is not a model — it is the plug between the model and the world.
- 3.MCP is an open client-server protocol. The host is the AI app.
- 4.The client is the connector inside that app. The server wraps a real system and speaks MCP.
Learn elsewhere
- →JSON-RPC message internals — covered in Transport
- →OAuth for remote servers — covered in Authentication
Real Example
Scenario
You deploy one GitHub MCP server. Cursor uses it to search code. Your weekend agent uses the same server to open issues. You did not write two GitHub integrations.
What you would do
In Model Context Protocol, apply Why MCP to this scenario: You deploy one GitHub MCP server. Identify the inputs, run the technique, validate the output, and note one thing you would monitor in production.
Commands
Commands to Remember
MCP = Model Context Protocol — USB-C for AI toolsHost = the AI app (Cursor, Claude Desktop, your agent)Client = connector inside the hostServer = wrapper around files, APIs, or a database
Cheat Sheet
Quick recap
quick ref- •MCP = one protocol for tools and data
- •Build the server once, reuse in many apps
- •Host / client / server are three different jobs
- •MCP is not the LLM itself
Common Mistakes
- ✕Thinking MCP is a new language model
- ✕Writing a new GitHub tool per agent instead of one server
- ✕Skipping the host vs client vs server distinction