Paste-your-data analytics over MCP, computed in code: csv_profile profiles columns and data quality, ab_test runs two-proportion z-tests, correlation and growth_rates cover the basics, with funnel_report, cohort_retention, and forecast_trend in the premium tier. No uploads, no external calls, no data retention.
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ab_testAb TestRun 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"}. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""} (for example {"error": "need visitors > 0 and 0 <= conversions <= visitors"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
| Name | Required | Description | Default |
|---|---|---|---|
| visitors_a | Yes | Visitors in variant A; must be at least 1. | |
| visitors_b | Yes | Visitors in variant B; must be at least 1. | |
| conversions_a | Yes | Conversions in variant A; 0 or more, at most visitors_a. | |
| conversions_b | Yes | Conversions in variant B; 0 or more, at most visitors_b. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
cohort_retentionCohort RetentionBuild 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": "..."}. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""} (for example {"error": "cohort '' must map to a list of numbers,"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
| Name | Required | Description | Default |
|---|---|---|---|
| cohorts | Yes | Mapping of cohort label to a list of counts, where counts[0] is the cohort size and counts[n] is users active in period n, e.g. {"2026-01": [1000, 400, 300]}. The first 24 cohorts are used. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
correlationCorrelationCompute 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": "..."}. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""} (for example {"error": "need two equal-length series of 3+ values"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
| Name | Required | Description | Default |
|---|---|---|---|
| x | Yes | First numeric series; at least 3 values, same length as y. | |
| y | Yes | Second numeric series; at least 3 values, same length as x. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
csv_profileCsv ProfileProfile 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"}. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""} (for example {"error": "delimiter must be a single character, e.g. ',' or ';'"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
| Name | Required | Description | Default |
|---|---|---|---|
| csv_text | Yes | Raw CSV content including a header row, pasted as a single string; the first 2000 data rows are profiled. | |
| delimiter | No | Field separator, exactly one character, e.g. "," or ";". Default ",". | , |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
forecast_trendForecast TrendForecast 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": "..."}. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""} (for example {"error": "need at least 4 historical values"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
| Name | Required | Description | Default |
|---|---|---|---|
| values | Yes | Ordered historical series, oldest first; at least 4 values. | |
| periods_ahead | No | How many future periods to forecast; values outside 1-12 are clamped. Default 3. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
funnel_reportFunnel ReportAnalyze 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": "..."}. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""} (for example {"error": "need at least 2 stages"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
| Name | Required | Description | Default |
|---|---|---|---|
| stages | Yes | Ordered mapping of stage name to count, top of funnel first; at least 2 stages with non-negative numeric values, e.g. {"Visited": 1000, "Signed up": 200}. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
growth_ratesGrowth RatesCompute 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}. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""} (for example {"error": "need at least 2 values"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
| Name | Required | Description | Default |
|---|---|---|---|
| values | Yes | Ordered numeric series, oldest first, at least 2 values, e.g. monthly revenue [1000, 1100, 1320]. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
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