# Moltline Data Desk

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

- name: com.moltlinestudio/data
- version: 1.2.0
- connection class: R0 (autonomous)
- trust: 84/100 flags: duplicate-repo
- quality: 91/100 (strong)
- owner: not claimed
- descriptor: https://api.protogrid.dev/v1/servers/com.moltlinestudio%2Fdata
- tools: https://api.protogrid.dev/v1/servers/com.moltlinestudio%2Fdata/tools

**An agent can connect right now, no human step.** Remote endpoint, no authentication, reachable on the last probe.

## Connection (mcpServers)

```json
{
  "mcpServers": {
    "data": {
      "type": "http",
      "url": "https://mcp.moltlinestudio.com/data"
    }
  }
}
```


## Trust

- hygiene: 60
- liveness: 99
- freshness: 85
- provenance: 85
- drivers: +repository +dns-namespace +website +reachable +uptime-98% ~updated-42d-ago -duplicate-repo(22 names)
- Derived from observable signals (official registry feed and our own credential-free probes); no code audit performed.

## Quality 91/100 (strong)

### protocol: 75 (weight 25)

- warn `protocol.modern`: newest supported version is 2025-11-25; 2026-07-28 not supported, older versions are deprecated until 2027-07-28
- n/a `protocol.stateless`: only applies to 2026-07-28 servers
- pass `protocol.transport`: streamable HTTP
- n/a `protocol.list_ttl`: only applies to 2026-07-28 servers

### authorization: n/a (weight 15)

- n/a `auth.prm`, `auth.as_metadata`, `auth.cimd`: no remote uses OAuth
- n/a `auth.secret_in_url`: no templated URL

### tool hygiene: 100 (weight 30)

- pass `tools.descriptions`: every tool has a description
- pass `tools.description_length`: descriptions are concise
- pass `tools.schemas`: every tool has an object input schema
- pass `tools.annotations`: 7 of 7 tools declare readOnlyHint or destructiveHint
- pass `tools.directory_hints`: every tool declares readOnlyHint, destructiveHint, idempotentHint and openWorldHint
- pass `tools.token_cost`: about 2,242 tokens to load every tool
- pass `tools.api_dump`: tools are not a one-to-one API dump

### stability: 100 (weight 15)

- pass `stability.changes`: no tool changes in 30 days
- pass `stability.rug_pull`: no tool changed its meaning under the same name

### dependencies: n/a (weight 15)

- n/a `deps.known_vulns`, `deps.mcp_sdk_version`, `deps.resolvable`: no npm or PyPI package

- tools: 7, about 2,242 tokens to load them all
- tool set hash: ccb60422c37a42ccd27114d5999e1848
- badge: [![protogrid quality](https://protogrid.dev/badge/com.moltlinestudio/data.svg)](https://protogrid.dev/servers/com.moltlinestudio/data)
- Checks run on what our credential-free, read-only probes observe; tools are never called and no code audit is performed.

### Tool changes

_No tool definition changes recorded._

## Remotes

- https://mcp.moltlinestudio.com/data (streamable-http, auth none, reachable true, uptime30d 0.9830508)

## Packages

_none_

## Tools (7)

- `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 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.
- `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 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.
- `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_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.
- `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 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.
- `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 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.
- `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 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.
- `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, 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.