Ai-Agents

An LLM agent may have access to a calculator, a browser, a code runner, GitHub, Slack, Jira, Google Drive, a database, and hundreds of internal APIs. Giving the agent more capabilities sounds useful. But there is a practical problem: every tool normally comes with a name, description, and input schema. If I place all of those definitions in every model request, the model has to read a large API catalog before it can answer a simple question. ...

Coding agents are useful when they can read code, edit files, run tests, and explain errors. But web development has a problem that does not fit neatly inside the file system: the real bug often lives in the browser. A React component may look fine in code but overflow on mobile. An API call may fail only after a specific login state. A button may be present in the DOM but not clickable. A performance issue may come from layout shifts, long tasks, font loading, image decoding, or network waterfalls. A console error may point to bundled JavaScript that needs source maps to be useful. ...

Introduction to MCP The Model Context Protocol (MCP) is an open standard for connecting AI assistants (like large language models) to the systems where data and tools live​ In essence, MCP aims to bridge the gap between isolated AI models and real-world data sources – think of it as a “USB-C for AI applications”, providing a universal way to plug an AI model into various databases, file systems, APIs, and other tools​. ...