Moltline Data Desk
Paste-your-data analytics: CSV profiling, A/B tests, correlation, growth. 4 of 7 free.
| Endpoint | https://mcp.moltlinestudio.com/data (streamable-http) |
|---|---|
| Registry version | 1.2.0 |
| Now | 7 tools · grade A (98) · pin ts_43e03ff2a9879e3b3f0deed253da28ad · protocol 2025-06-18 |
| First / last listed | 2026-10-28 06:39 UTC / 2026-10-28 06:39 UTC |
| Grade over time | 2026-10-28: A (98), 7 tools |
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Tools: csv_profile ab_test correlation growth_rates funnel_report cohort_retention forecast_trend
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, "unique": 2, "min": 29, ...}},
"quality_flags": ["..."], "note": "first 2000 rows profiled"}.
Use a params: csv_text*, delimiter |
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_at_95": true, "verdict": "B beats A:
statistically significant"}.
Use when exactly two variants eac params: conversions_a*, visitors_a*, conversions_b*, visitors_b* |
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 together. Reports
association only, never causation. Not for a single series over time
(growth_rate params: x*, y* |
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 two
variants (ab_test) and not for projecting future periods (forecast_trend). Errors: on invalid, params: values* |
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_dropoff": {...}, "recommendation": "..."}.
Use when stage counts descend through one funnel. Not for ret params: stages* |
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]},
"avg_curve_pct": [...], "reading": "..."}.
Use when each cohort has counts per period since acquisi params: cohorts* |
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": "..."}.
Use when a series is roughly linear and fit quality matters as much as the
projection. Not params: values*, periods_ahead |
Entries describe changes in tool definitions. They are not statements about a server, its quality or its author. The grade measures how the tool definitions fit model providers (token cost, schema compatibility, confusable names) as scored by the ToolFit engine at the time of the listing. JSON: append .json. Opt-out: how or hello@youraitools.co.