Shrink an AWS cost-triage MCP server
They rebuilt a read-only AWS cost checker with far fewer tools and cut the calls needed to answer a waste question from seven to two.
- What they did
- They made the same MCP server two ways against one live AWS account: first as 15 small boto3 wrappers, then as 4 broader tools focused on outcomes. In Kiro, both versions answered the same question about where money was being wasted. The smaller design used a tool that handled the internal checks and returned five fields for each finding, plus tighter inputs and a separate savings estimate tool.
- How it went
- The original setup took 7 tool calls, while the slimmer version finished in 2. The tool descriptions dropped from 7,898 characters to 897, an 88% reduction, and both versions identified the same three wasteful resources. The Elastic IP charge was still not confirmed.
- Tips
- A smaller tool set helps most when you have lots of tools, not just on a tiny test. Keep the server read-only and compare both call count and schema size before and after.
Try it yourself with Claude Code
Audit my MCP server in [path] that exposes [number] tools for [AWS cost data or similar]. First, map which tools overlap, which ones can be merged, and how to reduce the schema size without losing the ability to answer [example question]. Stop and show me the proposed tool list, the expected before/after call count, and any tradeoffs before changing files. If I approve, refactor the server, then run the same question and report the tool calls and findings, but do not send, delete, or modify anything without asking me first.
Fill in the [brackets]. Agents can make mistakes, so review anything before it is sent, paid or deleted.
Get 3 agent uses like this every morning
With the day's top AI news. Free, and one click to unsubscribe.
Related agent uses
Have the agent interview you before it writes code
A developer uses an agent to clear up vague requirements, handle small implementation choices, and keep them learning while the code is being built.
Create a CLI coding agent from a Python LLM base
Claude Code turned Simon Willison’s Python LLM template into a working CLI coding agent from two prompts.
Let an AI agent inspect a Windows crash dump
Use MCP to give an AI agent structured crash details from a Windows dump so it can track down the fault and propose a fix.
Check a codebase for redundant packages and polyfills
An agent can scan code and install commands to find dependencies that can be replaced with built-ins or better options.
Use an agent to inspect and explain COBOL on z/OS
A desktop agent connected to a mainframe to read COBOL, submit jobs, and summarize failures from spool output.
Run coding agents in parallel with tmux and Markdown specs
Manuel Schipper uses separate tmux panes and numbered Markdown specs to keep several coding agents moving at once.