Use a dedicated tool like Manus for initial research. It runs independently and provides traceable sources, allowing you to vet information before feeding it into your core OS (like Claude). This prevents your AI's memory from being 'polluted' with unverified or irrelevant data that could skew future results.

Providing too much raw information can confuse an AI and degrade its output. Before prompting with a large volume of text, use the AI itself to perform 'context compression.' Have it summarize the data into key facts and insights, creating a smaller, more potent context for your actual task.

When an AI's context window is nearly full, don't rely on its automatic compaction feature. Instead, proactively instruct the AI to summarize the current project state into a "process notes" file, then clear the context and have it read the summary to avoid losing key details.

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.

Users get frustrated when AI doesn't meet expectations. The correct mental model is to treat AI as a junior teammate requiring explicit instructions, defined tools, and context provided incrementally. This approach, which Claude Skills facilitate, prevents overwhelm and leads to better outcomes.

Don't assume AI can effectively perform a task that doesn't already have a well-defined standard operating procedure (SOP). The best use of AI is to infuse efficiency into individual steps of an existing, successful manual process, rather than expecting it to complete the entire process on its own.

To solve the problem of MCPs consuming excessive context, advanced AI clients like Cursor are implementing "dynamic tool calling." This uses a RAG-like approach to search for and load only the most relevant tools for a given user query, rather than pre-loading the entire available toolset.

Instead of jumping between apps, top PMs use a central tool like Claude Desktop or Cursor as a 'home base.' They connect it to other services (Jira, GitHub, Sanity) via MCPs, allowing them to perform tasks and retrieve information without breaking their flow state.

AI tools compound in value as they learn your context. Spreading usage across many platforms creates shallow data profiles everywhere and deep ones nowhere. This limits the quality and personalization of the AI's output, yielding generic results.