mcp-for-beginners and mcp-crash-course

These are complementary resources that serve different learning stages: the comprehensive multi-language curriculum provides foundational MCP concepts, while the hands-on crash course with project-based branches enables practical implementation through specific integration patterns like Streamable-HTTP and LangChain adapters.

mcp-for-beginners
76
Verified
mcp-crash-course
61
Established
Maintenance 25/25
Adoption 10/25
Maturity 16/25
Community 25/25
Maintenance 10/25
Adoption 10/25
Maturity 16/25
Community 25/25
Stars: 15,320
Forks: 4,986
Downloads:
Commits (30d): 171
Language: Jupyter Notebook
License: MIT
Stars: 142
Forks: 125
Downloads:
Commits (30d): 0
Language: Python
License: Apache-2.0
No Package No Dependents
No Package No Dependents

About mcp-for-beginners

microsoft/mcp-for-beginners

This open-source curriculum introduces the fundamentals of Model Context Protocol (MCP) through real-world, cross-language examples in .NET, Java, TypeScript, JavaScript, Rust and Python. Designed for developers, it focuses on practical techniques for building modular, scalable, and secure AI workflows from session setup to service orchestration.

The curriculum covers foundational MCP concepts like resource definitions, tool invocation patterns, and prompt templates across real-world server implementations. It emphasizes hands-on learning with code-along examples that demonstrate client-server communication via JSON-RPC over stdio transport, progressing from basic protocol mechanics to advanced patterns like dynamic resource discovery and error handling. The material aligns with the MCP specification (2025-11-25) and integrates with AI platforms like Claude, providing practical guidance on connecting MCP servers to LLM applications for tool use and context management.

About mcp-crash-course

emarco177/mcp-crash-course

Hands-on crash course for the Model Context Protocol (MCP) with project-based branches on Streamable-HTTP, LangChain adapters, and Docker.

Implements MCP through multiple transport mechanisms including Server-Sent Events (SSE) and stdio, with specialized branches teaching integration patterns for LangChain adapters and FastMCP 2.0 prompt handling. The learning structure uses chronologically-ordered Git commits within feature branches, allowing developers to trace architectural decisions step-by-step from initial server setup through production containerization with Docker.

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