- 5 mins read

2025-06-12 - Adventures in AIiii

Recently, while waiting in the drive-through at McDonalds, I started thinking about how much I was really spending there. Mint used to have this information readily available, and my credit card company’s website probably has a spending chart somewhere, but I also knew that McDonald’s sent pretty detailed email receipts. It had become a common stop for me, and while this could have been just an idle thought, I decided to use the opportunity to compare Claude Code and MCP to find an answer. In the age of AI, anything is possible.

Backing Up Your Email

Backups have always been important to me and while I haven’t been posting about it, I have continued to maintain my backups. https://github.com/GAM-team/got-your-back (GYB) is my current favorite tool for downloading my Gmail files to regular email files - it consistently works. I’ll also forgo directly connecting Gmail to Claude; I still prefer having some illusion of control over my data.

The first time setup is still daunting due to the complexity of having to create a Google Oauth app just for yourself, but once you get past that, you can download your emails locally to work with. I like this command to download year by year:

gyb --email email@gmail.com --action backup --search "after:2025/12/31 before:2026/01/01"

Now we have thousands of local emails to work with:

$ tree 2025 | head
2025
├── 1
│   ├── 1
│   │   ├── 19420838f30727e2.eml
│   │   ├── 19420cf0bab51def.eml
│   │   ├── 19421bda34835985.eml
│   │   ├── 194228c70a17dcab.eml
│   │   ├── 19422d8932fa4989.eml
│   │   ├── 194235b0a4bbfec5.eml
│   │   ├── 194235b13deb6a79.eml
...
167 directories, 1968 files

We confirm we have the right emails:

$ rg -l "mcdonalds"
2025/4/12/1962ad5a33cf606d.eml
2025/1/25/1949e723ad7b39d7.eml
2025/1/26/194a424e363c1ed9.eml
2025/1/19/1948037a64b788cc.eml

And some nicely formatted HTML receipts. They look nice and there is structure, butAnd while there is structure

mcdonalds.jpeg

Failed attempt: Claude Desktop with an MCP

Following the docs, I tried the Filesystem example MCP server, which is pretty easy to configure Node server that has basic filesystem commands like list_directory and search_files . Editing random JSON configs isn’t the best interface, but works and reflects the freshness of the MCP ecosystem.

Another surprising fact was understanding that MCP is a command (an HTTP server) that you locally run alongside Claude. I also understand that remote servers are still being specced out or improved, but seems like an overbuilt interface in some ways.

Initial attempts went as expected:

> Search all my files for emails about mcdonalds

I can see you have a Gmail backup directory with many .eml files organized by date. Let me search through these email files to find any mentions of McDonald’s. Since there are many files, I’ll search through some of them systematically.

Claude could understand there are files but the search command was only based on filename. It spun its wheels for several minutes trying to search every single file invidiually until it ran out of context.

While better prompting or a different MCP might have improved things, it felt slow and unsuccesful and I wasn’t getting the results I wanted.

Claude Code

Moving onto the Code terminal app general results more quickly. This tool seems to be growing in popularity and has a good set of overall abilities:

  • tool usage
  • planning mode
  • self-optimization with instruction files

Code had a similar approach blunt force approach of trying to add all emails to context and crashing out, but its planning and tool use were much better without prompting. Using grep to search versus anything else is the appropriate choice. It can break down tasks into smaller tasks that are more likely to be successful.

> Search all my email files for mcdonalds receipts, parse the emails, and summarize any useful data

⏺ I'll search for McDonald's receipts in your email files, parse them, and summarize the data. Let me start by creating a todo list to track this task.

⏺ Update Todos
  ⎿  ☐ Search email files for McDonald's receipts
     ☐ Parse found McDonald's receipt emails
     ☐ Extract and summarize useful data from receipts

⏺ Now let me search for McDonald's-related emails in your files.

⏺ Update Todos
  ⎿  ☐ Search email files for McDonald's receipts
     ☐ Parse found McDonald's receipt emails
     ☐ Extract and summarize useful data from receipts

⏺ List(.)
  ⎿  Listed 1429 paths (ctrl+r to expand)

⏺ Let me search for McDonald's-related emails using grep to find receipts:

⏺ Search(pattern: "(?i)(mcdonald|mcdonalds)", include: "*.eml")
  ⎿  Found 25 files (ctrl+r to expand)

⏺ Great! I found 25 email files containing McDonald's. Now let me update the todo status and start parsing these emails:

⏺ Update Todos
  ⎿  ☒ Search email files for McDonald's receipts
     ☐ Parse found McDonald's receipt emails
     ☐ Extract and summarize useful data from receipts

⏺ Now let me read several of these McDonald's emails to understand their structure and extract receipt data:

⏺ Read(2025/1/4/19432469c9580b0d.eml)
  ⎿  Read 1224 lines (ctrl+r to expand)

⏺ Read(2025/1/12/1945b7f96ef60553.eml)
  ⎿  Read 1314 lines (ctrl+r to expand)

After finding the correct set of working email receipts, it also performed some useful adhoc analysis in the terminal, but this where I manually directed it toward writing a Python script to parse the data and find useful information. Moving beyond the LLM context and working with real code and data is where Code really shines. I did a bit more cycles back and forth, but we quickly had a working Python script.

Emoji Summary

📊 OVERVIEW:
   Total Receipts: 24
   Date Range: 01/04/2025 to 06/07/2025
   Total Spent: $404.58
   Total Savings: $404.58
   Average Order: $16.86

🍔 MOST POPULAR ITEMS:
   1. Hash Browns: 78 orders
   2. Sausage & Egg McMuffin: 18 orders
   3. Sausage & Egg McMuffin      1P: 18 orders
   4. Sausage McMuffin: 6 orders
   5. 4 pc. Chicken McNuggets Happy Meal: 5 orders

💰 HIGHEST REVENUE ITEMS:
   1. Hash Browns: $167.91
   2. Sausage & Egg McMuffin: $109.62
   3. 4 pc. Chicken McNuggets Happy Meal: $46.83
   4. Large French Fries: $16.07
   5. Sausage McMuffin: $14.77

📅 MONTHLY SPENDING:
   2025-01: $102.55
   2025-02: $63.06
   2025-03: $92.39
   2025-04: $58.77
   2025-05: $41.49
   2025-06: $46.32

Github

Next Steps

Solving problems with LLMs is interesting in the tradeoffs

  • How to provide enough context or prompt for the LLM to make the “right” choices. Do you know the right choices up front?
  • There is value in cheap exploration and real results.
  • Is this making you dumber?

Adhoc Python scripts are nice but how more easily work with this data?