I was reading an Anthropic engineering post this winter that mentioned, almost in passing, that Claude Code's biggest token sink across their fleet is package-related queries . Every "how do I do X in Y", every npm install , every dependency audit. The model fetches the registry JSON, reads it, summarizes for itself, and only THEN answers you.

I started measuring it on my own agent traffic. 74% of the tokens my AI assistant burned on a typical "add this library" turn were the registry payload. Not my prompt. Not the answer. The middleware between them.

That's when I built DepScope MCP . And after six months in production with thousands of agents hitting it daily, I want to share what we've learned — including what's new in v0.9.0 that I shipped this morning.

The 3 things every AI install costs you

When your AI agent (Claude Code, Cursor, Windsurf, ChatGPT, whatever) suggests npm install <something> , three invisible costs land on your shoulders:

1. Tokens 🔥

The model needs context.…