Model Context Protocol Mcp Hands On With Agentic Ai 2034200
What is Model Context Protocol Mcp Hands On With Agentic Ai 2034200
The model-context-protocol-mcp-hands-on-with-agentic-ai-2034200 is a code repository for the LinkedIn Learning course ‘Model Context Protocol (MCP): Hands-On with Agentic AI’. It provides a framework for developers to enhance language models (LLMs) with agent behavior, enabling them to interact with data and applications consistently.
Use cases
Use cases include creating text analysis tools, weather forecasting applications, project documentation generators, and comparing machine learning models on platforms like GitHub.
How to use
To use the model-context-protocol-mcp-hands-on-with-agentic-ai-2034200, clone the repository to your local machine. You can then run the MCP servers in development mode using the MCP Inspector and test them in Claude Desktop and Cursor. The course provides guidance on building your own MCP servers using Python and TypeScript.
Key features
Key features include the ability to add agent behavior to LLMs, expose resources, tools, and prompts for complex operations, and the capability to connect with external APIs and perform advanced multi-step actions.
Where to use
The model-context-protocol-mcp-hands-on-with-agentic-ai-2034200 can be used in various fields such as software development, data analysis, and AI applications where enhanced interaction with language models is required.
Clients Supporting MCP
The following are the main client software that supports the Model Context Protocol. Click the link to visit the official website for more information.
Overview
What is Model Context Protocol Mcp Hands On With Agentic Ai 2034200
The model-context-protocol-mcp-hands-on-with-agentic-ai-2034200 is a code repository for the LinkedIn Learning course ‘Model Context Protocol (MCP): Hands-On with Agentic AI’. It provides a framework for developers to enhance language models (LLMs) with agent behavior, enabling them to interact with data and applications consistently.
Use cases
Use cases include creating text analysis tools, weather forecasting applications, project documentation generators, and comparing machine learning models on platforms like GitHub.
How to use
To use the model-context-protocol-mcp-hands-on-with-agentic-ai-2034200, clone the repository to your local machine. You can then run the MCP servers in development mode using the MCP Inspector and test them in Claude Desktop and Cursor. The course provides guidance on building your own MCP servers using Python and TypeScript.
Key features
Key features include the ability to add agent behavior to LLMs, expose resources, tools, and prompts for complex operations, and the capability to connect with external APIs and perform advanced multi-step actions.
Where to use
The model-context-protocol-mcp-hands-on-with-agentic-ai-2034200 can be used in various fields such as software development, data analysis, and AI applications where enhanced interaction with language models is required.
Clients Supporting MCP
The following are the main client software that supports the Model Context Protocol. Click the link to visit the official website for more information.
Content
Model Context Protocol (MCP): Hands-On with Agentic AI
This is the repository for the LinkedIn Learning course Model Context Protocol (MCP): Hands-On with Agentic AI. The full course is available from LinkedIn Learning.
Course Description
The Model Context Protocol (MCP) allows developers to add agent behavior to LLMs by providing a universal protocol providing context to language models so they can interface with data and applications in a consistent way. MCP servers expose resources (data), tools (actions), and prompts (instructions) for the LLM and the user to use in performing more complex operations. In this course you’ll explore how the MCP works in Claude Desktop to extend its functionality, and you’ll build your own MCP servers using Python and TypeScript to give LLMs new capabilities to do things on the computer, connect with external APIs, and perform advanced multi-step actions.
Instructions
You can work with these files in GitHub Codespaces or in an editor on your computer.
To run the MCP servers in development mode using the MCP Inspector and test them in Claude Desktop and Cursor, you need to clone the repository to your computer.
Contents
This repository contains folders with supporting files for the course.
Example MCP Servers
mcp-server-examples/text-assist: Python MCP server with tools to count characters and words in any given textmcp-server-examples/open-meteo-weather: Python MCP server with tools to get current and forecasted weather from Open-Meteomcp-server-examples/projectDocumenter: TypeScript MCP server with tools to summarize any project and generate comprehensive README.md documentsmcp-server-examples/gh-models-comparison: TypeScript MCP server with tools to list all available GitHub Models, compare models, and run completion comparisons between models
Hands-on Practice
gh-models-helper: Starting point for “Building an advanced MCP server using TypeScript”
MCP Server Templates
templates/mcp-server-python-template: README.md file with step-by-step instructions to set up a bare-bones MCP server using the Python MCP SDKtemplates/mcp-server-typescript-template: Scaffolding and instructions to set up a bare-bones MCP server using the TypeScript MCP SDK
Branches
This repository does not use branches.
Installing
Each folder has a README.md file with installation instructions.
Instructor
Morten Rand-Hendriksen
Principal Staff Instructor, Speaker, Web Designer, and Software Developer
Check out my other courses on LinkedIn Learning.
Dev Tools Supporting MCP
The following are the main code editors that support the Model Context Protocol. Click the link to visit the official website for more information.
