Now that we’ve compressed nearly all human knowledge into large language models, the next frontier is tool calling. Chaining together different AI tools enables automation. The shift from thinking to doing represents the real breakthrough in AI utility.
I’ve built more than 100 tools for myself, & they work most of the time, but not all the time. I’m not alone. Anthropic’s Economic Index report reveals that 77% of business use of Claude centers on full-task automation, not co-piloting.
Anthropic published documentation last week about token efficiency & re-architecting tools to optimize their use. The guidance was counterintuitive : instead of many simple tools with clear labels, create fewer, more complex tools.
Here are the seven email tools I built - Ruby scripts, each with a clear purpose. The “Safe Send Email” script was designed to prevent the AI from sending emails without approval.
draft_email.rb
send_email.rb
forward_email.rb
find_and_draft_reply.rb
read_email.rb
archive_emails.rb
safe_send_email.rb
Beautifully naive, simple, & clear, Shouldn’t a language model be able to read these & know exactly what I was asking it to do? But it’s not this simple!
Anthropic recommends creating complex tools. Their research shows that “requests save an average of 14% in output tokens, up to 70%" when using sophisticated, parameter-rich tools instead of simple ones. The reason? AI systems understand full context better than fragmented intent.
I spent the weekend consolidating all my tools into unified tools, like this one for email:
ruby unified_email_tool.rb \
--action send \
--to "john@company.com" \
--subject "Q4 Strategy Review" \
--body "..." \
--cc "team@company.com" \
--format concise
The impact on accuracy was immediate. Claude’s success rate approaches 100%. The system is faster. As a result, I’m using far fewer tokens with a more efficient system.
Here’s my current mental model:
| People Need | AI Systems Need |
|---|---|
| Cognitive chunking | Complete context |
| Progressive disclosure | Parameter-rich interfaces |
When I redesigned for AI cognition rather than human intuition, everything improved. My CRM operations, calendar management, & database workflows all became more reliable when consolidated into comprehensive, parameter-heavy tools. Accuracy improved, so the total cost was reduced significantly.
But don’t ask me to use the tools. I’m now a bit lost amidst the complexity. This is an inevitable corollary of working at higher levels of abstraction, no longer deeply understanding the machine.
We spent decades making software simple for people. Now we’re learning to make it complex for AI.