About dotplot-mcp
dotplot-mcp helps product teams analyze user-level behavior with a daily, per-user dot plot rather than relying only on aggregate charts. It computes metrics in code and has Claude interpret findings, with safeguards for small samples and causal claims.
Key features
- 01Generate product analysis reports
- 02Analyze user-by-day activity plots
- 03Add missing event tracking
- 04Compare with a previous report
- 05Withhold conclusions for small groups
- 06Refuse vanity value events
Use cases
- 01Inspect retention patterns by user
- 02Identify user actions associated with value
- 03Add missing product event logging
- 04Compare current analysis with an earlier report
MCP support
It provides Dotplot analysis through an MCP server that can be installed for Claude. The README describes Claude using the server to find project data, analyze it, and produce reports.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
Tools
16Saved tool list · updated 2w ago
| Parameter | Type | Required | Description |
|---|---|---|---|
| lang | string | No | |
| csv_path | any | No | |
| output_path | string | No | |
| value_event | any | No |
Details
From the project READMEDot 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
Or do it by hand
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.
No product to analyze yet? Try the sample
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
Comments
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