James Collins

In modern B2B sales, the “Context Gap” can limit deal effectiveness. Even with comprehensive CRM records—emails, call notes, and deal stages—sales teams often miss critical real-time developments in their prospects’ businesses. If a lead recently raised funding or launched a new product and outreach does not reflect it,

AI-assisted development tools such as Cursor and Claude have changed how developers interact with their codebases. These tools excel at generating, refactoring, and explaining code by leveraging the local project context. However, they are still limited when it comes to retrieving fresh, external information such as updated documentation, security advisories,

The SerpApi Model Context Protocol (MCP) server provides a unified search tool for AI agents. It exposes SerpApi’s web search APIs (Google, Bing, Yahoo, DuckDuckGo, Yandex, Baidu, YouTube, eBay, Walmart, and more) through a standard MCP interface. Any MCP-compatible client can call a single search tool to retrieve live

Bing Copilot (also known as Copilot Search) is Microsoft’s new AI-powered search assistant. It combines organic and generative search to provide concise, conversational answers to queries. In practice, Bing Copilot returns a summary card with a short answer (often with an illustrative image/video) and a numbered list of

🔗Heads up! You can now start using SerpApi MCP. Modern AI agents and large language models (LLMs) are exceptionally capable at reasoning and generating text, but they often struggle to access real-time data or external tools without a lot of custom engineering. Traditionally, integrating an LLM with a database, an

Search engines and AI are rapidly reshaping how businesses find opportunities online. Today’s “AI agents” – systems that autonomously browse and query the web on a user’s behalf – are already changing SEO best practices. For example, industry data shows that ChatGPT’s user agents doubled their web-search activity in

Modern travel planning demands fresh, structured signals and predictable automation. Flight availability, hotel inventory, and local recommendations are time-sensitive and often encoded in vertical search outputs. AI agents can directly assist in solving this problem. A reliable AI travel agent will combine the reasoning capabilities of a language model with

Modern language models are effective at synthesis but do not inherently provide fresh, verifiable information. Connecting a model to web search creates an autonomous AI agent that closes the gap: it enables current sources, systematic coverage of a topic, and traceable answers. This post describes a compact research agent that