MCP in Claude Agents

• Key points are in the visual diagram above.

MCP is how you plug GitHub without a custom tool — and how supply-chain attacks arrive.

USB devices: useful, and you do not plug in a stick from the parking lot.

Scenario

USB devices: useful, and you do not plug in a stick from the parking lot.

Solution

In Claude Agent SDK, apply MCP in Claude Agents to this scenario: USB devices: useful, and you do not plug in a stick from the parking lot. 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 MCP in Claude Agents (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 MCP in Claude Agents happens in the code.

1# MCP in Claude Agents — 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 MCP in Claude Agents8    model="gpt-4o-mini",9    messages=[10        {"role": "system", "content": "You explain mcp in claude agents clearly."},11        {"role": "user", "content": f"What is mcp in claude agents?"},12    ],13    temperature=0,14)15print(response.choices[0].message.content)  # show output for debugging

• Skipping evaluation for MCP in Claude Agents before production
• No logging or tracing around claude mcp steps
• Ignoring cost and latency implications

Quick recap — the most important points from this module.