Mastering Model Context Protocol (MCP): A Practical Guide

Trace the evolution of AI data integrations from manual prompts to json-rpc tool calls, and see how model context protocol standardizes external connectors with Stripe as an example.

Learn how JSON RPC uses JSON to encode remote procedure calls, delivering lightweight, transport-agnostic actions through a standard request with jsonrpc, method, params, and optional id (notification when absent).

Clone the MCP project from GitHub, open it in VSCode, set up a Python virtual environment with uv, install dependencies from pyproject.toml, and run the first MVP server.

Discover how tool calling lets a language model access real-time information by invoking external tools and APIs, then define json tool schemas, bind tools, and assemble final answers.

Create your first MCP server with server.py, expose an add tool via json-rpc, and run the server on port 8000 using a transport such as stdio, sx, or streamable http.

Master the MCP server workflow by connecting to port 8000 and the MCP route, setting json-rpc headers and content type, then performing initialize, notifications initialized, and tools call.

Connect to the MCP server from a Python client using a streamable http transport. Initialize session to obtain the MCP session id and invoke a tool with a=21 and b=21.

Explore stdio, server-sent events over ssh, and streamable http as transport methods for the model context protocol, highlighting local testing, long-lived connections, and stateless, scalable serverless design.

Build a local stdio server and client in MCP using the Stdio server parameters and client factory to call add (a=7, b=5) via async IO and print the result.

Explore building a server and client using the MCP framework with server-sent events (SSE), including setup, imports, and a comparison of SSE with streamable http.

Build a server and client with streamable http by toggling stateless http and session mode in server.py, initialize sessions when needed, then test on port 8000 with client.py.

Explore how tools, resources, and prompts serve as MCP extension points. Tools enable structured external calls, resources offer controlled data sources, and prompts guide user-driven workflows.

Build an MCP server with resources and prompts using the MCP class, server.py, and client.py, exposing a stateless in-memory dish database via resource and prompt decorators.

Mastering the MCP client shows async IO to discover resources, tools, and prompts, read resources, and fetch prompts in a stateless HTTP session.

Discover how fast MCP v2 streamlines building MCP servers and clients in Python. Compare with the official MCP Python SDK and learn why streamable HTTP replaces SSE.

Discover how the context object enables stateful http sessions, streaming real-time logs and incremental progress updates to keep clients informed during long-running tasks.

Build a stateful MCP server with a fast MCP context to process a list of items, log progress, and report structured progress updates through the MCP endpoint.

Use the context object in the MCP client to receive progress updates and intermediate steps via a message and log handler, enabled by a stateful MCP server.