Let me paint you a picture. You're 25 minutes into a Claude Code session. You've been building a workflow, debugging a Docker issue, reading logs, fetching API docs. Things are flowing. Then the model starts forgetting what file it was editing. It asks you to repeat something you said five minutes ago. The dreaded compaction kicks in — and suddenly your AI assistant has amnesia. Sound familiar? I've been running 39,000+ deployed n8n workflows across a multi-brand portfolio. My daily work involves Docker, PostgreSQL, Traefik, Cloudflare Tunnels, Python scrapers, and a dozen other moving pieces. When I'm deep in a Claude Code session debugging a FreePBX integration or migrating Airtable bases, context window management isn't a nice-to-have — it's the difference between a productive session and starting over from scratch every 30 minutes. That's why I want to talk about **Context Mode** (https://github.com/mksglu/context-mode) — what it actually does, how it compares to working without it, and why it matters even if you're not using Claude Code. --- ## The Problem Nobody Talks About Every MCP tool call dumps raw data into your context window. Here's what that looks like in real numbers: - A **Playwright snapshot** costs 56 KB - **20 GitHub issues** cost 59 KB - **One access log** — 45 KB - A **500-row CSV** — 85 KB Your context window is 200K tokens. Sounds like a lot until you realize that with 80+ tools active, you're already burning 143K tokens (72%) before your first message even goes through. Then every tool response eats into what's left. After 30 minutes of real work, 40% of your usable context is gone. And when the agent compacts the conversation to free up space? It forgets which files it was editing, what tasks are in progress, and what you last asked for. This is the reality of working without context management. Every single one of us who uses AI coding tools has hit this wall. --- ## What Context Mode Actually Does