io.github.brownglasses/dotplot-mcp

Product analytics for user flows: aha moments, retention, funnels, per-user dot plots. YC method.

Open source Open in the app JSON README (API)

About

Product analytics for user flows: aha moments, retention, funnels, per-user dot plots. YC method.

Details

Kind: MCP servers
Topic: Marketing & analytics
Publisher: brownglasses
Origin: official
Category: ferramentas
Transport: local
Version: 0.1.4
Stars: 1
Last push: 2026-08-14T01:35:41Z
Repository state: ativo
Language: Python
License: MIT
Added: 2026-08-29 03:02:32
Updated: 2026-08-29 03:02:32
Origin id: io.github.brownglasses/dotplot-mcp

README

# Dot Plot MCP

> See individual users, not aggregate charts.

**English** | [한국어](README.ko.md)

![report](.github/report_en.png)

## Install

Paste this to your agent and it sets everything up:

```
Set up Dotplot (MCP + skills) — instructions are here https://dotplot-reports.vercel.app/setup.md
```

<details>
<summary>Or do it by hand</summary>

```bash
claude mcp add --scope user dotplot -- uvx dotplot-mcp
```

</details>

## Use

Say this in any project:

> **"Analyze my product"**

You get the report above. That's it.

Or use the slash commands:

```
/dotplot-analyze-product    full analysis and report
/dotplot-add-tracking       find and write the logging you're missing
/dotplot-whats-changed      compare with the previous report
```

Claude finds your data, picks the action that means "this user got value", and
writes the report. No events table needed — your `orders` table already is one:

```sql
SELECT user_id, created_at::date AS date, 'purchase' AS event FROM orders
UNION ALL
SELECT user_id, added_at::date, 'add_to_wishlist' FROM wishlist_items
```

Nothing tracked yet? Say **"add the tracking I'm missing"** and Claude reads your
code, writes the logging that's absent, and tells you when to come back.

<details>
<summary>No product to analyze yet? Try the sample</summary>

```bash
git clone https://github.com/brownglasses/dotplot-mcp && cd dotplot-mcp
uv run sample_data.py   # 40 fake users with a pattern planted in them
uv run harness.py       # watch the whole pipeline run
```

</details>

## Why this exists

DAU/MAU charts go "up and to the right" as long as new users arrive — even when
nobody stays. Until you have hundreds of users, the most informative dashboard is
[YC's dot plot](https://www.youtube.com/watch?v=e5-6rEwzxLs) (David Lieb):
**one row per user, one cell per day.**

Four rules keep it honest:

- **Code computes, AI only interprets** — same data, same numbers, every time
- **Small samples withhold judgment** — under 5 users in a group, it says nothing
- **Correlation isn't cause** — every finding ships with "test this before you believe it"
- **Vanity metrics are refused** — pick `open_app` as your value event and the code says no

Reports come out in your language (English, 한국어, 日本語 built in; anything else
translated on the fly with the numbers verified intact).

## More

- [What each tool does, and the anonymous benchmark](docs/tools.md)
- [How it's built](docs/architecture.md)

MIT

<!-- mcp-name: io.github.brownglasses/dotplot-mcp -->