Why we built BotScope
A new generation of customers asks Claude, ChatGPT, and Perplexity instead of Google. We needed an instrument to watch what the models were saying.
Research, methodology, and longitudinal observations from inside the observatory. We share what we see, why we think it matters, and the tools we use to see it more clearly.
A new generation of customers asks Claude, ChatGPT, and Perplexity instead of Google. We needed an instrument to watch what the models were saying.
APIs have been the backbone of software integration for decades. Now the Model Context Protocol (MCP) is emerging as a new standard built specifically for AI agents. How do they compare, and when should you use each?
The Model Context Protocol (MCP) is quickly becoming one of the most talked-about standards in AI. But what exactly is it, how does it work, and why should you care? This guide breaks it all down in plain language.
With the rapid evolution of AI technologies, methods such as Retrieval-Augmented Generation (RAG) and Model Context Protocol (MCP) are becoming central in improving AI-generated content. While both methods aim to enhance AIs ability to provide accurate and contextually relevant outputs, they operate in distinctly different ways. Lets look into the key differences and their implications.
The rise of AI agents—autonomous systems that can interact with digital environments on behalf of users—is reshaping the internet as we know it. As these agents become more prevalent in how people search, shop, and interact online, **website design must evolve** to accommodate this new kind of "user."
In the fast-moving world of AI, new technologies and protocols are emerging that promise to reshape how machines interact with the web. One of the most exciting developments is the **Model Context Protocol (MCP)** — a standard designed to improve how AI models understand, navigate, and retrieve content from websites.