Master Generative AI: MCP Developer Bootcamp

Explore the history of ai from machine learning to deep learning and large language models, then examine ai clients and retrieval augmented generation to understand the model context protocol.

Learn how retrieval augmented generation (RAG) lets you augment LLMs with external and internal company data using embeddings and a vector database, yielding up-to-date, citable responses.

The lecture surveys AI clients—apps, assistants, and autonomous agents—showing how tools and APIs enable function calling, with a registry, execution engine, and chained workflows via model context protocol.

Learn how function calling connects large language models to external tools, using official and third-party SDKs, with a weather example, execution logic, and the need for a modal context protocol.

Build a weather assistant with the GPT Script SDK, using function calls and a built-in HTTP get tool to fetch current weather for a city and return a final response.

Explore how the model context protocol, an open standard, standardizes secure ai data connections via MCP clients and servers, enabling tool discovery and granular permissions.

Explore MCP security fundamentals, including authentication, authorization, and encryption, guard against on-path attacks, and implement secure idp integrations with the resource server model.

Develop an MCP weather tool server in Python, implement get current weather, and demonstrate tool listing, two client versions, and OpenAI GPT four integration via standard IO and decorators.

Set up an isolated MCP client and server environment, install MCP and OpenAI SDK via the UV package manager, and initialize the weather MCP server project.

Develop a simple MCP client that launches and tests a weather server via standard IO, using asynchronous IO and pathlib, verifying connectivity to the City of London weather tool.

Presenting MCP server version two, a high-level, slimmer approach that uses decorators to automate listing and calling tools, with dynamic permissions and notification capabilities for flexible server behavior.

Test the weather MCP server directly via the command line interface in dev mode, then explore the inspector UI to interact with prompts, routes, and tools.

Connect an MCP server with an OpenAI GPT LLM to create a weather-aware AI assistant, test a Python client for get current weather, and enable tool-driven queries.

Connect Claude Desktop to an MCP weather service by configuring the server path in the Claude settings, starting the server, and using the weather assistant tool to fetch current conditions.

Learn to build a flight booking simulation with an MCP server and integrated tools, enabling search, booking, cancellation, and logging via a cloud desktop interface.

Explore a fake flight booking MCP server with in-memory data, simulated airlines and airports, and tools for search flights, flight details, book, status, and cancel.

Learn to run an MCP server remotely on port 3000 with a single http endpoint and streamable http transport, enabling json rpc calls and real-time bidirectional updates.

Set up the MCP server for remote connections by configuring MCP host and port, starting on port 3000, enabling streamable HTTP, and testing with the MCP inspector.

Learn to test an MCP remote proxy from cloud desktop by configuring a remote server, handling authentication, and using the proxy to reach weather service integrations.

Secure the MCP server with api key authentication and a scalable architecture. Learn fast api, Starlette, uvicorn, api key middleware, and health and docs endpoints.

Update the Python MCP client to connect to a remote server using an API key, header authentication, and the Streamable HTTP client, with code changes and usage examples.

Learn to implement a client credentials OAuth flow with Keycloak as the authorization server, issuing JSON web tokens and bearer tokens for an MCP resource server.

Start a local Keycloak IdP with Docker, expose port 8080, and set admin credentials; create an MCP realm with MCP up and MCP server clients, and import their JSON configurations.

Test the MCP server with auth via the MCP inspector, obtain an access token for the authorization header, and verify access using the inspector tools.

Discover how to enable cors in web apps by implementing cors middleware, handling preflight options request, and configuring Keycloak web origins to enforce same-origin policy in development and production.

Test the course server by running a python http server on localhost and performing an options request. Authenticate with keycloak and test MCP calls to OpenAI GPT-4 and weather data.

Explore MCP resources to give the LLM read-only context from text or binary data, expanding knowledge bases with document content and database lookups.

Add resources to the MCP server, including an about markdown, a todo management guide PDF, and dynamic templates for meeting tasks; fetch metadata and PDFs from storage like S3.

Test the todo MCP server prompts using cloud desktop, exploring tool workflows for resources, prompts, and templates, and performing table-based prioritization analyses.

Run the refactored MCP server and REST API from a downloaded zip, set up a Python uv environment, and test unauthenticated endpoints with curl before using the API key.

Explore a refactored MCP server through a practical code walkthrough, detailing environment setup, rest API integration, token validation with keycloak, auth flows, logging, graceful shutdown, and modular MCP components.