com.moltlinestudio/data · v1.2.0 · data-and-databases
Moltline Data Desk is healthy: it completed an MCP handshake in 2017 ms and exposes 7 tools. It scores 61/100.
Paste-your-data analytics: CSV profiling, A/B tests, correlation, growth. 4 of 7 free.
Endpoint: https://mcp.moltlinestudio.com/data (streamable-http)
Repository · Website · JSON · Add to my inventory
| health | 17.9 / 25 |
| authEase | 15 / 15 |
| toolQuality | 15 / 15 |
| maintenance | 5 / 15 |
| adoption | 0 / 10 |
| usage | 0 / 10 |
| security | 8 / 10 |
Scoring v0.3, computed 2026-10-14T20:40:53.019Z.
| Checked (UTC) | Status | Latency | Error |
|---|---|---|---|
| 2026-10-14T20:40:43.138Z | Healthy | 2017 |
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_ |
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 |
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 |
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_dropo |
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": |