Wrap-up and resources
Key Takeaways
- 1. Building an AI agent in Python: An agent = model + tools + loop + limits. The model decides; your code runs, validates and caps the number of steps.
- 2. Building an MCP server in Python: You write the server once and any MCP client uses it. Tools for actions, resources for reading, and a precise description is the model's interface.
Glossary
| Term | Definition | Part |
|---|---|---|
| Agent | A language model in a loop with tools. | 1 |
| tool_use | The model's reply when it asks to run a tool. | 1 |
| tool_result | The result you return to the model with the same id. | 1 |
| MCP | An open standard for connecting tools and data to AI apps. | 2 |
| stdio | Local transport: the client runs the server as a process and they talk over stdin/stdout. | 2 |
| tools/list | The client's request to discover the server's tools. | 2 |
Resources for this lesson
Grouped by type. Start with the official docs for precision, or an article or video for the explanation.
Official docs3
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