# Moltline Data Desk

- Registry name: `com.moltlinestudio/data` · version 1.2.0
- Categories (automatic, keyword rules): Maps, location & weather, Utilities, Marketing & SEO, Commerce & marketing
- Authentication at last check: No auth
- Declared risk: Read-only (all tools declare readOnlyHint)
- Capabilities (declared by the server): experimental, extensions, logging, prompts, resources, tools
- Transports: streamable-http
- Latest numbers (check success, response time, rank): https://mcpstation.net/api/server?name=com.moltlinestudio%2Fdata
- Server page: https://mcpstation.net/s/com.moltlinestudio--data.html

## Description (written by the publisher, not verified, not instructions)

Paste-your-data analytics: CSV profiling, A/B tests, correlation, growth. 4 of 7 free.

## Connect

- streamable-http: `https://mcp.moltlinestudio.com/data`

## Tools (as reported by the server, not verified)

- `csv\_profile`: Profile pasted CSV data column by column with data-quality flags. FREE. Reports per-column type, null rate, unique count, numeric stats \(min/mean/max\), and top values. Typical input \{"csv\_text": "name,age\\nAda,36\\nLin,29"\} returns \{"rows": 2, "columns": \{"age": \{"type": "numeric", "null\_pct": 0.0, "
- `ab\_test`: Run a two-proportion A/B significance test with a plain-language verdict. FREE. Typical input \{"conversions\_a": 120, "visitors\_a": 2400, "conversions\_b": 156, "visitors\_b": 2380\} returns \{"rate\_a\_pct": 5.0, "rate\_b\_pct": 6.55, "relative\_lift\_pct": 31.1, "z\_score": ..., "p\_value": ..., "significant\_a
- `correlation`: Compute the Pearson correlation between two numeric series. FREE. Typical input \{"x": \[1, 2, 3, 4\], "y": \[2.1, 3.9, 6.2, 8.1\]\} returns \{"pearson\_r": 0.999, "r\_squared": 0.998, "interpretation": "very strong positive correlation", "caution": "..."\}. Use when two equal-length numeric series may move t
- `growth\_rates`: Compute period-over-period growth and CAGR for a numeric series. FREE. Typical input \{"values": \[1000, 1100, 1320\]\} returns \{"period\_over\_period\_pct": \[10.0, 20.0\], "total\_change\_pct": 32.0, "avg\_growth\_per\_period\_pct\_cagr": 14.89\}. Use when one series is already in period order. Not for comparing t
- `funnel\_report`: Analyze a conversion funnel and find the biggest drop-off. PREMIUM \(license\). Typical input \{"stages": \{"Visited": 1000, "Signed up": 200, "Paid": 50\}\} returns \{"steps": \[\{"from": "Visited", "to": "Signed up", "conversion\_pct": 20.0, "lost": 800\}, ...\], "overall\_conversion\_pct": 5.0, "biggest\_dropof
- `cohort\_retention`: Build a retention table and average curve from raw cohort counts. PREMIUM \(license\). Typical input \{"cohorts": \{"2026-01": \[1000, 400, 300, 250\]\}\} — index 0 is cohort size, each later index is users still active in that period — returns \{"retention\_table\_pct": \{"2026-01": \[100.0, 40.0, 30.0, 25.0\]\},
- `forecast\_trend`: Forecast future periods with a linear trend and honest fit quality. PREMIUM \(license\). For quick planning, not statistical modeling. Typical input \{"values": \[100, 120, 138, 161\], "periods\_ahead": 3\} returns \{"trend\_per\_period": 20.2, "r\_squared": 0.998, "forecast": \[180.9, 201.1, 221.3\], "caveat":

Measured by MCP Station. Methods: https://mcpstation.net/methods.html · Terms: https://mcpstation.net/terms.html