model_context_protocol_training
A Slidev presentation and developer guide to the Model Context Protocol (MCP) by Anthropic, covering AI integration, LLM tool use, core concepts, and implementation examples for AI agents. #mcp #a
claude mcp add jakeyshakey-model_context_protocol_training
How to use
This repository serves as a comprehensive guide and presentation for Model Context Protocol (MCP) integration, using Slidev to illustrate the core architecture, implementation patterns, and practical examples. It covers how MCP enables standardized, JSON-RPC based tool integration between LLMs (such as Claude) and external tools, with a focus on context management and extensibility. Although the repository centers on a learning guide and tutorials rather than hosting a running MCP server, it includes Python examples for implementing MCP clients and servers and demonstrates use cases like data analysis pipelines and Tableau integrations. To get started with MCP concepts, review the slides to understand the architecture, then refer to the accompanying Python examples to see how to build tool adapters, how to structure JSON-RPC messages, and how to orchestrate tool use within agentive AI workflows.
How to install
Prerequisites:
- Node.js and npm (or yarn) installed on your machine
- Basic familiarity with command-line usage
Installation steps:
- Clone the repository: git clone <repository-url>
- Navigate to the project directory: cd <repository-directory>
- Install dependencies (Slidev project):
npm install
or if you prefer yarn:
yarn install - Run the development server to view the presentation:
npm run dev
or with yarn:
yarn dev - Open the URL printed in the terminal (typically http://localhost:5173) to view the slides.
Notes:
- The repository focuses on MCP concepts and Python examples for implementing MCP clients/servers rather than providing a ready-to-run MCP server out of the box.
Additional notes
Tips and notes:
- This guide emphasizes MCP concepts such as standardized communication, context management, and extensibility for tool integration with LLMs.
- If you’re implementing your own MCP server, rely on Python examples referenced in the guide to understand how to structure agents and tool adapters.
- The repository references Tableau as a use case for data analysis workflows; consider how MCP can facilitate tool invocation and context sharing with external services.
- Explore the official MCP and Claude API documentation linked in the resources for deeper technical details.
- If you encounter environment-specific issues, ensure Node.js/npm versions are compatible with Slidev and verify network access for dependencies.
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