com.moltlinestudio/data

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Au dernier audit MCP Hub, le serveur MCP « com.moltlinestudio/data » est classé « Non encore testé ». Référencé dans l'annuaire. Seul le pentest comportemental fait foi, et il n'a pas encore été exécuté sur ce serveur. Le verdict est réévalué en continu ; consultez la fiche pour l'état à jour.

Méthode : MCP Hub analyse la description de chaque outil (couche 1) et, pour le verdict fort, exécute le serveur dans une micro-VM isolée avec leurres et réseau bloqué (couche 2). On publie des noms et des compteurs, jamais le contenu brut renvoyé par un outil.

Les 7 outils

• ab_test≈ 448 tokens

  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 each have a trial count and a conversion count. Not for continuous outcomes such as revenue per user, and not for three or more variants. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"} (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.

  Paramètres, retour & exemple (4)

  Paramètres

  • visitors_a · integer · requis
    Visitors in variant A; must be at least 1.
  • visitors_b · integer · requis
    Visitors in variant B; must be at least 1.
  • conversions_a · integer · requis
    Conversions in variant A; 0 or more, at most visitors_a.
  • conversions_b · integer · requis
    Conversions in variant B; 0 or more, at most visitors_b.

  Exemple d'appel

  {"name": "ab_test", "arguments": {"visitors_a": 1, "visitors_b": 1, "conversions_a": 1, "conversions_b": 1}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• cohort_retention≈ 367 tokens

  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 acquisition. Not for a one-pass funnel (funnel_report). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"} (for example {"error": "cohort '<value>' 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.

  Paramètres, retour & exemple (1)

  Paramètres

  • cohorts · object · requis
    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.

  Exemple d'appel

  {"name": "cohort_retention", "arguments": {"cohorts": {}}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• correlation≈ 354 tokens

  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_rates). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"} (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.

  Paramètres, retour & exemple (2)

  Paramètres

  • x · array · requis
    First numeric series; at least 3 values, same length as y.
  • y · array · requis
    Second numeric series; at least 3 values, same length as x.

  Exemple d'appel

  {"name": "correlation", "arguments": {"x": [], "y": []}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• csv_profile≈ 404 tokens

  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 as the first look at unfamiliar tabular data. Not for testing a hypothesis (ab_test, correlation) and not for time-ordered trends (growth_rates, forecast_trend). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"} (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.

  Paramètres, retour & exemple (2)

  Paramètres

  • csv_text · string · requis
    Raw CSV content including a header row, pasted as a single string; the first 2000 data rows are profiled.
  • delimiter · string · optionnel
    Field separator, exactly one character, e.g. "," or ";". Default ",".

  Exemple d'appel

  {"name": "csv_profile", "arguments": {"csv_text": "texte"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• forecast_trend≈ 380 tokens

  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 for seasonal or cyclical data, and not for measuring growth already observed (growth_rates). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"} (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.

  Paramètres, retour & exemple (2)

  Paramètres

  • values · array · requis
    Ordered historical series, oldest first; at least 4 values.
  • periods_ahead · integer · optionnel
    How many future periods to forecast; values outside 1-12 are clamped. Default 3.

  Exemple d'appel

  {"name": "forecast_trend", "arguments": {"values": []}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• funnel_report≈ 361 tokens

  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 retention over time (cohort_retention) and not for two-variant comparisons (ab_test). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"} (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.

  Paramètres, retour & exemple (1)

  Paramètres

  • stages · object · requis
    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}.

  Exemple d'appel

  {"name": "funnel_report", "arguments": {"stages": {}}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• growth_rates≈ 317 tokens

  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, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"} (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.

  Paramètres, retour & exemple (1)

  Paramètres

  • values · array · requis
    Ordered numeric series, oldest first, at least 2 values, e.g. monthly revenue [1000, 1100, 1320].

  Exemple d'appel

  {"name": "growth_rates", "arguments": {"values": []}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

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Source

https://mcp.moltlinestudio.com/data