Weiter zum Inhalt
StartseitePython

Kurs

Introduction to Model Context Protocol (MCP)

FortgeschrittenSchwierigkeitsgrad
Aktualisiert 03/2026
Integrate AI/LLM applications with APIs, databases, and filesystems easier than ever before with the Model Context Protocol (MCP).
Kurs kostenlos starten
PythonArtificial Intelligence3 Std.11 Videos34 Übungen2,850 XPLeistungsnachweis

Kostenloses Konto erstellen

oder

Durch Klick auf die Schaltfläche akzeptierst du unsere Nutzungsbedingungen, unsere Datenschutzrichtlinie und die Speicherung deiner Daten in den USA.

Beliebt bei Lernenden in Tausenden Unternehmen

Group

Training für 2 oder mehr Personen?

Probiere es mit DataCamp for Business

Kursbeschreibung

Why Learn the Model Context Protocol?

Large language models can't access real-time data or take actions on their own, and wiring each tool or API with custom code doesn't scale. The Model Context Protocol (MCP) solves this with a single, standardized way for AI applications to connect to external tools, data, and services—often called "the USB-C port for AI." In this course, you'll build MCP servers and clients from scratch in Python and wire them to an LLM.

How Do I Build and Connect My First MCP Server?

You'll start by learning the MCP architecture—host, client, and server—and the three primitives every server exposes: tools, resources, and prompts. Then you'll build a currency converter server using FastMCP, add docstrings and type hints so an LLM can discover your tools, and write an async Python client that lists and calls those tools over stdio transport.

How Do I Give an LLM Real-Time Tools and Context?

Tools alone aren't enough—models also need data and behavioral instructions. You'll add resources for read-only context and prompts to guide the model when inputs are vague, then wire all three primitives into an OpenAI LLM using the five-step tool-calling workflow so it can answer confidently or ask for clarification when it should.

How Do I Take MCP Servers to Production?

Real-world servers need more than happy-path code. You'll swap file-based resources for database-backed queries, add request timeouts, structured error handling, and secure API authentication that keeps keys server-side. Finally, you'll connect to a third-party MCP server and see that the same client code works with any server that speaks the protocol.

Voraussetzungen

Introduction to APIs in PythonWriting Functions in Python
1

The Building Blocks of MCP

Discover how MCP can make integrating AI applications with surrounding systems easier than ever before! Learn about how MCP works, how to define your own MCP tools, and build the bridge between client and server.
Kapitel starten
2

MCP-Enabled LLM Applications

3

Preparing MCP Servers for Production

Find out what it takes to get MCP servers into production by taking a deep-dive into databases and APIs in MCP servers, and the additional considerations that they bring. Finally, integrate third-party MCPs securely and reliably, so you don't have to recreate the wheel for your favorite integrations.
Kapitel starten
Introduction to Model Context Protocol (MCP)
Kurs
abgeschlossen

Leistungsnachweis verdienen

Füge diesen Fähigkeitsnachweis zu Deinem LinkedIn-Profil, Anschreiben oder Lebenslauf hinzu
Teile es auf Social Media und in Deiner Leistungsbeurteilung
Jetzt anmelden

Schließe dich 19 Millionen Lernenden an und starte Introduction to Model Context Protocol (MCP) heute!

Kostenloses Konto erstellen

oder

Durch Klick auf die Schaltfläche akzeptierst du unsere Nutzungsbedingungen, unsere Datenschutzrichtlinie und die Speicherung deiner Daten in den USA.

DataCamp gibt es auch für Mobilgeräte

Mit unseren Kursen für Mobilgeräte und täglichen Programmier-Challenges erweiterst du deine Datenkompetenz von unterwegs.