Training course · Model Context Protocol (MCP) · Python Development

Model Context Protocol (MCP) for Python Developers

Build and connect MCP servers securely: tools, resources and prompts for Claude Code, Cursor, Copilot and your own AI agents.

Duration
2 days (14 hrs)
Format
Online or on-site
Language
German or English
Participants
open dates or exclusive for your team
Certificate
Certificate of attendance

Overview

“Model Context Protocol (MCP) for Python Developers” is designed for roles such as Python back-end developers working with Django, Flask or FastAPI, Automation and scripting specialists in IT and business units and Data and ML engineers maintaining production Python code and shows step by step how to use Model Context Protocol (MCP) productively, safely and transparently in Python Development – from the fundamentals to advanced workflows.

The Model Context Protocol is an open standard that gives AI applications a uniform way to access external tools, data sources and workflows. Instead of building separate integrations for every assistant, you implement an MCP server once and use it in Claude Code, Claude Desktop, Cursor, GitHub Copilot, VS Code and your own agents. In this two-day course you learn the host, client and server architecture, the three core primitives of tools, resources and prompts, and the stdio and Streamable HTTP transports, applying each of them directly in your own code.

On day one you use the official TypeScript or Python SDK to build an MCP server that exposes internal APIs, databases or files to AI assistants in a controlled way, and you test it with the MCP Inspector. Day two covers remote servers, OAuth-based authorisation, security risks such as prompt injection and tool poisoning, and operations, logging and governance in an enterprise setting. You learn to write tool descriptions that models use reliably, and you leave knowing how to integrate MCP servers safely into your development and agent landscape.

Python powers web back ends, automation, data pipelines and AI applications – often inside the same organisation, which makes codebases notoriously diverse. AI coding assistants and agents have a particularly visible impact here: they write pytest suites around your existing fixtures, add type hints until mypy passes cleanly, migrate legacy scripts to modern packaging with pyproject.toml and walk you through an unfamiliar Django or FastAPI project in minutes rather than days. The key is steering these tools with the right context: project conventions, Ruff rules, test commands and architectural decisions belong in instruction files and prompts. Once you master that, you can delegate routine work with confidence and focus on design, review and business logic.

The course is delivered live – either online in a virtual classroom or on-site in our training rooms in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart and Kaiserslautern. We can also run it exclusively for your team.

Who is this course for?

  • Python back-end developers working with Django, Flask or FastAPI
  • Automation and scripting specialists in IT and business units
  • Data and ML engineers maintaining production Python code
  • Tech leads responsible for code quality and review processes
  • Developers modernising legacy Python codebases

Typical challenges we address

  • Ageing codebases with no type hints and patchy test coverage
  • Dependency sprawl across requirements.txt, Poetry, uv and virtual environments
  • Time-consuming reviews and inconsistent style despite linters and formatters
  • Onboarding into unfamiliar projects that rely on undocumented tribal knowledge

What you will be able to do

  • You explain the MCP architecture and choose between tools, resources and prompts for each use case.
  • You develop your own MCP server in TypeScript or Python and test it systematically.
  • You connect MCP servers to Claude Code, Cursor, Copilot and custom agents.
  • You run remote servers using Streamable HTTP and OAuth-based authorisation.
  • Have AI agents reliably deliver pytest suites, type hints and refactorings across your Python codebase.
  • Encode project conventions, tooling such as Ruff, mypy and uv, and test commands so the assistant follows them consistently.
  • Review generated Python code systematically for correctness, security and maintainability before it is merged.

Course outline

Day 1

Module 1 Architecture and core concepts
  • Roles of host, client and server and the lifecycle of a connection
  • JSON-RPC messages, capability negotiation and notifications
  • Tools, resources and prompts: choosing the right primitive
  • Client features such as sampling, roots and elicitation at a glance
Module 2 Building your own MCP server
  • Setting up a server with the official TypeScript or Python SDK
  • Defining tools cleanly with JSON Schema for inputs and outputs
  • Exposing resources and resource templates for documents, records and configuration
  • Testing and debugging with the MCP Inspector and meaningful error messages
Module 3 Connecting to Claude Code, Cursor, Copilot and agents
  • Configuring local stdio servers and remote servers in clients
  • Project-level and user-level configuration, approvals and permissions
  • Writing tool descriptions that models reliably select correctly
  • Integrating MCP servers into custom agents built with the Claude Agent SDK or OpenAI Agents SDK
Module 4 Transport, authorisation and operations
  • Streamable HTTP, session management and stateless servers
  • OAuth-based authorisation, token validation and integration with existing identity providers
  • Deploying as containers, on serverless platforms or behind a gateway
  • Logging, monitoring, rate limiting and versioning tool interfaces

Day 2

Module 5 Security and governance
  • Threats: prompt injection via tool results, tool poisoning, confused deputy
  • Least privilege, confirmation requirements and sandboxing
  • Reviewing and approving third-party servers within the organisation
  • Data protection, logging of personal data and requirements under GDPR and the EU AI Act
Module 6 Practice lab: Python Development
  • Generating pytest tests
  • Type hints and mypy clean-up
  • Django migrations and ORM refactoring
  • FastAPI endpoints with Pydantic
  • Modernising legacy scripts
  • Understanding and documenting a codebase
Module 7 Data protection, quality & transfer to daily work
  • Define which source code may be sent to cloud-hosted models, keep secrets from .env files strictly out of context, and check generated code for licence carry-over and newly introduced PyPI dependencies (typosquatting, supply-chain risk). For products with digital elements, obligations under the Cyber Resilience Act are becoming increasingly relevant.
  • Ground rules for safe AI use in your team
  • Checking results: facts, sources, tests and the four-eyes principle
  • Personal action plan for the next 30 days
  • Open questions, outlook and resources for further learning

Practical scenarios from your field

Generating pytest tests

The assistant inspects functions and existing fixtures, writes parametrised pytest tests covering edge cases and mocks for external services, then runs them iteratively until they pass and reach the coverage you are aiming for.

Type hints and mypy clean-up

Untyped code is annotated module by module with type hints, TypedDicts and Protocols. The agent runs mypy or Pyright, interprets the output and fixes issues step by step without changing runtime behaviour.

Django migrations and ORM refactoring

Model changes are planned together with data migrations, makemigrations conflicts are resolved and N+1 queries are fixed with select_related or prefetch_related. The AI flags risks to existing production data before you deploy.

FastAPI endpoints with Pydantic

From a functional description or an OpenAPI draft, the assistant produces Pydantic models, routers, dependency injection and validation logic, complete with error handling and matching tests using the TestClient.

Modernising legacy scripts

Old scripts become proper packages with pyproject.toml, global state is removed, argparse or Click interfaces are added and Ruff rules applied. The agent documents every change clearly in the commit history.

Understanding and documenting a codebase

The assistant explores an unfamiliar repository, produces architecture overviews, explains data flows between modules and fills in missing docstrings in Google or NumPy style so new team members become productive faster.

Possible hands-on projects

Log analysis CLI

A Typer-based command-line tool that reads application logs, clusters error patterns and outputs a concise summary as Markdown or JSON – shipped with tests and proper packaging.

FastAPI microservice with a database

A small REST service with SQLAlchemy, Alembic migrations, Pydantic validation and a Docker setup, planned, implemented and tested end to end by an AI agent.

Dependency audit script

A script that lists all project dependencies, checks for outdated or vulnerable packages against public advisory databases and produces a prioritised upgrade report.

Prerequisites

Solid programming skills in TypeScript/JavaScript or Python and a basic understanding of HTTP APIs and JSON. Some experience using AI assistants in day-to-day development is helpful. Solid Python skills plus hands-on experience with Git, virtual environments and at least one framework such as Django, Flask or FastAPI.

Technical requirements

A laptop with Node.js or Python, Git and a code editor, plus access to at least one MCP-capable client such as Claude Code, Claude Desktop, Cursor or VS Code with Copilot.

Data protection & compliance

Define which source code may be sent to cloud-hosted models, keep secrets from .env files strictly out of context, and check generated code for licence carry-over and newly introduced PyPI dependencies (typosquatting, supply-chain risk). For products with digital elements, obligations under the Cyber Resilience Act are becoming increasingly relevant.

Suggested dates

All dates are suggestions – your preferred date is always possible.

  • Mon, 23 November 2026 – Tue, 24 November 2026 Online Request
  • Wed, 2 December 2026 – Thu, 3 December 2026 Augsburg Request
  • Thu, 17 December 2026 – Fri, 18 December 2026 Online Request
  • Thu, 21 January 2027 – Fri, 22 January 2027 Kaiserslautern Request
  • Thu, 4 February 2027 – Fri, 5 February 2027 Online Request
  • Tue, 9 February 2027 – Wed, 10 February 2027 Bremen Request

Frequently asked questions

Why would I need my own MCP server?

Whenever AI assistants need access to internal systems such as ticketing tools, databases, documentation or line-of-business applications. Your own server defines precisely which actions and data are available and can then be reused across several clients. That avoids isolated one-off integrations and keeps access firmly under your control.

Which programming language does the course use?

You can choose between TypeScript and Python, both of which have official SDKs. The concepts are identical and all exercises are available in both versions. Developers working in other languages still benefit, because the protocol is language-agnostic and SDKs for other languages follow a similar structure.

How secure is MCP for enterprise use?

The protocol provides mechanisms for authorisation and permissions, but real-world security depends on implementation and operations. Risks such as prompt injection through tool results or over-privileged servers are genuine. We cover concrete countermeasures, approval processes for third-party servers and how to handle personal data.

Does AI-assisted coding work in large Django monoliths?

Yes, provided the agent gets the right context. On the course you learn to create instruction files describing architecture rules, app structure and test commands, and to scope tasks so the assistant works on individual apps or modules. Changes stay manageable and reviewable even when the overall project is very large.

Will AI replace my code reviews?

No. AI assistants speed up writing code and can prepare reviews by pointing out missing tests or risky sections, but functional and security accountability stays with your team. We show how to combine review checklists, static analysis and tests so that generated code meets exactly the same quality bar as hand-written code.

How much does “Model Context Protocol (MCP) for Python Developers” cost?

The course costs a flat €949 plus VAT per participant – whether online or on-site. This includes 2 training day(s) with a live trainer, course materials, a lab environment and a certificate of attendance.

Can I suggest my own date?

Yes. Our suggested dates are just a guide. In the request form you can always describe your preferred date or period in free text – we check availability and confirm within one working day.

Is the course online or on-site?

Both are possible: live online in a virtual classroom or on-site in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart or Kaiserslautern. We can also run the course exclusively for your team.

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