I've been using the DevTools MCP for months now, but it's extremely token heavy. Is there an alternative that provides the same amount of detail when it comes to reading back network requests?
This is one place where human intuition helps a ton today. If you can find the most relevant snippets and give the AI just the right context, it does a much better job.
The problem with MCP is that you are paying the price in token usage, even if you are not using the MCP server. Why would anybody want that?
And no, the tool search function recently introduced by Anthropic does not completely solve this problem.
But then I wonder, where the balance is between a bunch of small tool calls, vs one larger one.
I recall some recent discussion here on hn on big data analysis
The real problem isn't MCP vs CLI, it's that MCP originally loaded every tool definition into context upfront. A typical multi-server setup (GitHub, Slack, Sentry, Grafana, Splunk) consumes ~55K tokens in definitions before Claude does any work. Tool selection accuracy also degrades past 30-50 tools.
Anthropic's Tool Search fixes this with per-tool lazy loading - tools are defined with defer_loading: true, Claude only sees a search index, and full schemas load on demand for the 3-5 tools actually needed. 85% token reduction. The original "everything upfront" design was wrong, but the protocol is catching up.