Shrinkflation Sleuth

A Gemini agent that checks whether a grocery product has shrunk and works out how much more you now pay per unit.

Portfolio

I built it for the Google Cloud Rapid Agent Hackathon (Elastic track) in three days, with Google ADK, Gemini, Elasticsearch and the Elastic MCP server.

Shrinkflation is when a product gets smaller and the price stays the same. It's an economic idea that has interested me for a long time. A lot of companies use it to make any tiny bit of extra profit they can, and a smaller bag at the same price is easy to miss. Capital One Shopping research estimates it has touched roughly a third of grocery items. The number that shows it is the price per ounce, so that's what this agent reports.

How it works

The agent is one Google ADK LlmAgent with six playbooks written into its prompt: product lookup, brand investigation, category analysis, shopping-list audit, trend dashboard and data discovery. Gemini picks the playbook. All data access goes through five read-only Elastic MCP tools: search, esql, list_indices, get_mappings and get_shards.

A product lookup is three tool calls:

1. search the products index (fuzzy multi_match) to get a product_id.
2. esql over product_history for that id, computing price per unit at each point.
3. search shrinkflation_alerts for the documented change and its source. Reddit-only reports are marked unverified.

The answer leads with the effective price increase, meaning the change in price per unit. When the price holds, it's always larger than the shrinkage: 20% less product at the same price is 25% more per unit.

flowchart LR
    U([Shopper]) -->|question| A["Google ADK LlmAgent<br/>Gemini Flash<br/>6 playbooks"]
    A -->|MCP tool calls| M{{"Elastic MCP server<br/>search · esql · list_indices<br/>get_mappings · get_shards"}}
    M --> ES[("Elasticsearch<br/>products · product_history<br/>shrinkflation_alerts · categories")]
    P["data_pipeline.py<br/>curated seed table"] -->|JSON| ES
    P -->|same JSON| X["Sleuth Explorer<br/>static site, GitHub Pages"]
    A -->|sourced verdict| U

On Cloud Run it's two services: the official docker.elastic.co/mcp/elasticsearch image in HTTP mode, and this repo's FastAPI app (main.py), which serves the ADK chat UI and reaches MCP through ELASTIC_MCP_URL. Locally, with ELASTIC_MCP_URL unset, the agent spawns es_mcp_server.py, a small Python server with the same five tools over stdio, so you don't need Docker. Tool-by-tool details are in docs/architecture.md.

The live demo

The full agent needs an Elasticsearch cluster and a Gemini key. Neither can be hosted publicly for free, and a public LLM endpoint would expose the key, so the live demo is a static page instead. It runs five of the six playbooks in plain JavaScript over the same JSON files the agent queries, and each panel shows the tool calls the agent would make. There's no LLM in it.

Results

• 34 products from 16 brands in 7 categories. 29 had a documented size cut (24 verified), and 5 are unchanged controls.
• Across the 29 cuts, price per unit went up 12.2% on average, from an average shrinkage of 10.2%.
• Worst case: Haribo Goldbears went from 5 oz to 4 oz at the same $1.99, a 25% increase per ounce.
• By category (verified events only), Frozen & Dairy averages 14.3% shrinkage, but that's a single event. Beverages is next at 13.0% across 3.

Limitations

• I never measured the agent's answers. I spot-checked the six query types by hand. There's no eval of answer quality or tool-call accuracy.
• The data is small and partly synthetic. Sizes, dates and sources come from public reports. Prices are representative, and the history points around each change are interpolated. See data/README.md.
• The prompt hardcodes the four index names and their fields, so a different schema means editing the prompt.
• The Elastic Cloud cluster from the hackathon is shut down. To run the agent, load the data into your own cluster with make index.
• The explorer's search imitates Elasticsearch's multi_match with fuzziness: AUTO. It isn't Elasticsearch, so some queries rank differently.
• The Cloud Run deploy uses --allow-unauthenticated and the app allows any CORS origin. That was fine for a hackathon demo. Put auth in front of it before running it anywhere real.

Setup

The demo needs no accounts:

git clone https://github.com/ShawnJoshua03/shrinkflation-sleuth && cd shrinkflation-sleuth
make setup        # venv + dependencies
make serve        # http://localhost:8080

This is the beginning of the README. Read the rest at the original. Read the full README ↗