Building MCP Servers on Cloudflare: A Practical Guide
A practical guide to building MCP servers on Cloudflare Workers, covering architecture, code examples with D1 and R2, and production patterns for exposing Cloudflare resources to AI assistants.
Model Context Protocol
Building Agents with MCP: The Model Context Protocol in Practice
How to design, build, and ship Model Context Protocol servers that turn LLMs into reliable, tool-using agents — with patterns drawn from Claude Desktop, Cursor, and real production deployments.
Mastering Claude AI: Free Courses That Transform Developers, Educators, and Everyday Users into AI Powerhouses
Mastering Claude AI: Free Courses That Transform Developers, Educators, and Everyday Users into AI Powerhouses In an era where artificial intelligence is reshaping industries from software engineering to education, Anthropic’s free learning academy stands out as a game-changer. Hosted on a dedicated platform, these courses demystify Claude—their flagship AI model—offering hands-on training in everything from basic usage to advanced API integrations and ethical AI collaboration. Unlike scattered tutorials, this structured curriculum provides certificates upon completion, bridging the gap between theoretical knowledge and practical application.[1][4] ...
Mastering MCP Tool Discovery: Zero-to-Hero Tutorial for LLM Agent Builders
In the rapidly evolving world of LLM agent architectures, the Model Context Protocol (MCP) has emerged as a game-changing standard for enabling seamless, dynamic interactions between AI models and external tools. This comprehensive tutorial takes you from zero knowledge to hero-level implementation of MCP Tool Discovery—the mechanism that powers intelligent, scalable agentic systems. Whether you’re building production-grade AI agents, enhancing IDEs like VS Code, or creating Claude Desktop extensions, mastering tool discovery is essential for creating truly autonomous LLM workflows.[1][7] ...
Docker AI Agents & MCP Deep Dive: Zero-to-Production Guide
Introduction The rise of AI agents has created a fundamental challenge: how do you connect dozens of LLMs to hundreds of external tools without writing custom integrations for every combination? This is the “N×M problem”—managing connections between N models and M tools becomes exponentially complex. The Model Context Protocol (MCP) solves this by providing a standardized interface between AI systems and external capabilities. Docker’s integration with MCP takes this further by containerizing MCP servers, adding centralized management via the MCP Gateway, and enabling dynamic tool discovery. ...