Why MCP

Before MCP, every agent framework reinvented tool wiring. MCP provides one protocol for Slack, GitHub, databases, and custom APIs — build once, connect everywhere.

MCP is USB-C for AI tools — one port, many devices, instead of a different cable for every gadget.

Scenario

Instead of writing custom GitHub integration for your agent, Cursor, and a CLI tool separately, you deploy one MCP server that all three clients use.

Solution

In Model Context Protocol, apply Why MCP to this scenario: Instead of writing custom GitHub integration for your agent, Cursor, and a CLI tool separately, you deploy one MCP server that all three clients use. Identify the inputs, run the technique, validate the output, and note one thing you would monitor in production.

Do this before moving to the next module — reading alone is not enough.

Open the Code Walkthrough below and run it locally. Change one parameter related to Why MCP (e.g. model, temperature, top_k, or tool name), observe the difference in output, and write 2–3 sentences explaining what changed.

Highlighted lines show where Why MCP happens in the code.

1# Why MCP — minimal example2from openai import OpenAI3
4client = OpenAI()  # create API client5
6# Ask the model to explain this topic7response = client.chat.completions.create(  # core API call for Why MCP8    model="gpt-4o-mini",9    messages=[10        {"role": "system", "content": "You explain why mcp clearly."},11        {"role": "user", "content": f"What is why mcp?"},12    ],13    temperature=0,14)15print(response.choices[0].message.content)  # show output for debugging

• Treating Why MCP as a black box without evaluation
• Ignoring cost and latency in production
• Skipping error handling for why mcp

Quick recap — the most important points from this module.