The technical term "MCP" (Model Component Provider) is confusing. It's simpler and more accurate to think of them as connectors that give AI tools access to knowledge within your other apps and the ability to perform actions in them.
Go beyond using Claude Projects for just knowledge retrieval. A power-user technique is to load them with detailed, sequential instructions on how specific MCP tools should be used in a workflow, dramatically improving the agent's reliability and output quality.
Bridge the physical-digital divide in family scheduling. Take a picture of a physical wall calendar and feed it to an AI agent like Claude. Using MCPs for Google Calendar, the agent can parse the image and automatically create or update digital events, even adding buffer time for travel.
A powerful personal AI use case is creating interview prep materials. By feeding a tool like Google's NotebookLM with the job description, company info, and market context, you can generate a personalized audio summary that coaches an applicant, boosting their preparedness and confidence.
While agentic AI can handle complex tasks described in natural language, it often fails on processes that take too long (e.g., over seven minutes). Traditional, deterministic automation workflows (like a standard Zap) are more reliable for these long-running or asynchronous jobs.
When building AI workflows that process non-text files like PDFs or HTML, consider using Google's Gemini models. They are specifically strong at ingesting and analyzing various file types, often outperforming other major models for these specific use cases.
Create a virtuous cycle for your knowledge base. Use AI to analyze closed support tickets, identify the core issue and solution, and propose a new FAQ entry if one doesn't exist. A human then reviews and approves the suggestion, continuously improving the AI's data source.
To find high-impact AI opportunities, reframe the goal from speed to quality. Ask what a perfect team with unlimited time would do. This helps identify transformative workflows, like analyzing every support ticket to improve documentation, rather than just doing existing tasks faster.