Server data from the Official MCP Registry
Product analytics for user flows: aha moments, retention, funnels, per-user dot plots. YC method.
About
Product analytics for user flows: aha moments, retention, funnels, per-user dot plots. YC method.
Security Report
Valid MCP server (1 strong, 2 medium validity signals). 5 known CVEs in dependencies (0 critical, 5 high severity) Package registry verified. Imported from the Official MCP Registry.
4 files analyzed · 6 issues found
Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.
Permissions Required
This plugin requests these system permissions. Most are normal for its category.
How to Install
Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-brownglasses-dotplot-mcp": {
"args": [
"dotplot-mcp"
],
"command": "uvx"
}
}
}Documentation
View on GitHubFrom the project's GitHub README.
Dot Plot MCP
See individual users, not aggregate charts.
English | 한국어

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
claude mcp add --scope user dotplot -- uvx dotplot-mcp
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:
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.
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
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 (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_appas 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
MIT
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