Frequently Asked Questions
Common Questions About Getting Started With the Model Context Protocol
Everything you need to know before registering.
What do I need to know before getting started with MCP? +
To get started with MCP in this workshop you need intermediate Python experience (comfortable with classes, async functions, and APIs), basic familiarity with LLMs and how to call an AI API, and a laptop with Python 3.10 or later installed. You do not need prior MCP knowledge, prior multi-agent experience, or any specific framework experience. The workshop builds from these foundations.
What is the first thing I will build when getting started with MCP? +
The first thing you build is a simple MCP server in Python that exposes one tool with a typed schema. This first server demonstrates the core MCP concepts: how to define a tool, how to handle an invocation, and how to return a structured result. From this foundation, the workshop builds progressively more complex MCP patterns until you have a complete multi-agent system.
How does MCP differ from just calling AI APIs directly? +
Calling AI APIs directly means writing custom integration code for every tool and data source your agents need. MCP provides a standard protocol so any MCP-compatible agent can use any MCP-compatible tool or data source without custom integration. MCP also provides typed schemas that validate agent communication, structured error handling that makes failures informative rather than opaque, and a discovery mechanism that lets orchestrating agents find available capabilities dynamically.
What MCP concepts are most important to understand when getting started? +
The most important MCP concepts for beginners are: the server-client model (agents as servers, orchestrators as clients), tool definitions (typed schemas for agent capabilities), resource definitions (typed access to shared data), the prompt template system (structured agent instructions), and error types (structured failure communication). The workshop introduces each concept in the order you encounter it while building the Glass-Box Context Engine.
How long does it take to get a productive MCP setup working? +
With this workshop's guidance, you have a productive MCP setup working within the first 90 minutes of the 6-hour session. By the end of the second module you have multiple MCP servers orchestrated by a client. The remaining four modules build on this foundation to add RAG integration, memory management, safeguards, and production deployment. You leave with a complete working system.
What resources should I use to continue learning MCP after this workshop? +
After this workshop the best resources are the official MCP specification and documentation for reference, the Anthropic blog for updates on MCP evolution, the MCP Python SDK GitHub repository for examples and community patterns, and the Glass-Box Context Engine code you build during the workshop as a reference implementation for your own projects. The instructor also covers the MCP community resources during the session.
