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com.moltlinestudio/data

REMOTE · MCP.MOLTLINESTUDIO.COM · SCANNED AUG 17

Hosted MCP server for data analysis: CSV profiling, A/B tests, cohorts, funnels, trend forecasts.

70 Trust /100
Trust breakdown (6 categories)

How this component scores in each security and reliability category. Every signal is checked automatically against the live server, and we only credit what we can confirm. How we score →

Endpoint Security83
Transport & Reachability100
Schema Quality & AI Usability60
  • AI-judged instruction clarity (good).Pass
  • Context-footprint check failed: tool/resource definitions use about 1709 tokens (~244/item across 7 items; 7 tools + 0 resources), over budget; trim descriptions and params. See how to fix → Fail
  • Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management0
  • Stability not yet verified: not enough scan history yet (needs a 30-day window).Unverified
Tool Coverage100
  • 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
  • 100% of tool parameters carry a description.Pass
  • Structured output schemas are declared (100% of tools); any adoption earns full credit.Pass
Capabilities100
  • Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.Pass

Unverified: 1 category

A category scored 0 because we could not verify it: authentication we do not have, an unreachable endpoint, or not enough scan history. We only credit what we can confirm.

Install

Add this component to your MCP client. Where a client-specific snippet is available, pick your client below and copy it straight into your config; otherwise use the connection detail shown.

remote · mcp.moltlinestudio.com

# add to Claude Code
claude mcp add --transport http com-moltlinestudio-data https://mcp.moltlinestudio.com/data
# ~/.codex/config.toml
[mcp_servers.com-moltlinestudio-data]
url = "https://mcp.moltlinestudio.com/data"
// opencode.json
{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "com-moltlinestudio-data": {
      "type": "remote",
      "url": "https://mcp.moltlinestudio.com/data",
      "enabled": true
    }
  }
}
# add to OpenClaw
openclaw mcp add com-moltlinestudio-data --url https://mcp.moltlinestudio.com/data --transport streamable-http
# ~/.hermes/config.yaml
mcp_servers:
  com-moltlinestudio-data:
    url: "https://mcp.moltlinestudio.com/data"
// mcp.json
{
  "mcpServers": {
    "com-moltlinestudio-data": {
      "type": "http",
      "url": "https://mcp.moltlinestudio.com/data"
    }
  }
}

The mcpServers block is a cross-client convention. Remote transports vary, so check your client's docs.

Changelog

Every change we have recorded for this component, newest first. Security-relevant changes are always shown. ▲ marks a change for the better, ▼ a change for the worse; unmarked changes are neutral.

  • 17 Aug 26 70

    First indexed and scored.

Diagnostics

Diagnostic detail from the automated scan of this channel: what the scanner observed at each step, so you can see exactly where a check passed or failed. It is informational only and never changes the trust score.

Captured 17 Aug 2026 · Probed https://mcp.moltlinestudio.com/data

TLS valid

Negotiated TLS 1.3 with TLS_AES_128_GCM_SHA256 .

Subject Issuer Valid from Valid until Key Signature Serial
CN=mcp.moltlinestudio.com CN=YE1,O=Let's Encrypt,C=US 16 Aug 2026 14 Nov 2026 ECDSA 256 ECDSA-SHA384 6c3ed2184cc1622eb0659f596575816a5c8
SANs: mcp.moltlinestudio.com
CN=YE1,O=Let's Encrypt,C=US (CA) CN=Root YE,O=ISRG,C=US 3 Sept 2025 2 Sept 2028 ECDSA 384 ECDSA-SHA384 5ddd70dd31f801c85c186a7a04b80afe
CN=Root YE,O=ISRG,C=US (CA) CN=ISRG Root X2,O=Internet Security Research Group,C=US 13 May 2026 2 Sept 2032 ECDSA 384 ECDSA-SHA384 872165fc34b6e5fba8add5b3705fb53a
CN=ISRG Root X2,O=Internet Security Research Group,C=US (CA) CN=ISRG Root X1,O=Internet Security Research Group,C=US 13 May 2026 2 Sept 2032 ECDSA 384 SHA256-RSA 6c8f1dc727c7117f7baf853ac980f9cd
DNSSEC secure

Validation of mcp.moltlinestudio.com. Secure

Zone DS Keys Algorithms Outcome
. trust_anchor 20326, 38696 8, 8 Verified
com. present 19718 13 Verified
moltlinestudio.com. present 2371 13 Verified
mcp.moltlinestudio.com. Verified address RRset verified with the apex keys
Authentication No authorisation required

The endpoint answered without asking for a token. Anyone who knows the URL can reach it.

Result No authorisation required
HTTP status 200
Header Value
strict-transport-security max-age=31536000
x-content-type-options nosniff
Transports 2 probes
Transport URL Outcome Status Location
streamable-http https://mcp.moltlinestudio.com/data Verified 200
http (plaintext) http://mcp.moltlinestudio.com/data HTTPS enforced 308 https://mcp.moltlinestudio.com/data
MCP tools · 7 exposed · ~1,667 tokens

The tools this component advertises to a client, with an estimated token cost for each. Expand a tool to see its parameters and schema. The per-tool counts are indicative and are not scored directly; the schema's total context footprint is one signal in Schema Quality & AI Usability.

Tool Tokens
ab_test ~292

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"}. 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.

NameTypeReqDescription
conversions_aintegeryesConversions in variant A; 0 or more, at most visitors_a.
conversions_bintegeryesConversions in variant B; 0 or more, at most visitors_b.
visitors_aintegeryesVisitors in variant A; must be at least 1.
visitors_bintegeryesVisitors in variant B; must be at least 1.

Structured output declared, but exposes no named fields.

No examples provided.

cohort_retention ~264

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": "..."}. 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.

NameTypeReqDescription
cohortsobjectyesMapping 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.

Structured output declared, but exposes no named fields.

No examples provided.

correlation ~214

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": "..."}. 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.

NameTypeReqDescription
xarrayyesFirst numeric series; at least 3 values, same length as y.
yarrayyesSecond numeric series; at least 3 values, same length as x.

Structured output declared, but exposes no named fields.

No examples provided.

csv_profile ~260

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"}. 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.

NameTypeReqDescription
csv_textstringyesRaw CSV content including a header row, pasted as a single string; the first 2000 data rows are profiled.
delimiterstringField separator, exactly one character, e.g. "," or ";". Default ",".

Structured output declared, but exposes no named fields.

No examples provided.

forecast_trend ~224

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": "..."}. 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.

NameTypeReqDescription
periods_aheadintegerHow many future periods to forecast; values outside 1-12 are clamped. Default 3.
valuesarrayyesOrdered historical series, oldest first; at least 4 values.

Structured output declared, but exposes no named fields.

No examples provided.

funnel_report ~225

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": "..."}. 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.

NameTypeReqDescription
stagesobjectyesOrdered 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}.

Structured output declared, but exposes no named fields.

No examples provided.

growth_rates ~188

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}. 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.

NameTypeReqDescription
valuesarrayyesOrdered numeric series, oldest first, at least 2 values, e.g. monthly revenue [1000, 1100, 1320].

Structured output declared, but exposes no named fields.

No examples provided.