Quattr

correlate_domains

Read-only

Is metric A related to metric B across two different data areas? Correlate two metrics from DIFFERENT domains over time — for example, do pages with worse Core Web Vitals get fewer Search Console clicks, or does rising market share track rising traffic. Fetches each metric as a time series, lines them up by week (or day/month), and returns Pearson and/or Spearman correlation with a significance value. Use for "does X move with Y?" questions that span search, web analytics, paid, market share or page experience. Correlation is not causation — the result includes a caveat.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "metric_a": "<metric_a>",
      "metric_b": "<metric_b>",
      "date_range": "<date_range>"
    },
    "name": "correlate_domains"
  }
}

Input Schema

Name	Required	Description	Default
`method`	No	Which correlation to compute: pearson (linear), spearman (rank-based, robust to outliers), or both (default).	both
`metric_a`	Yes
`metric_b`	Yes
`date_range`	Yes	Explicit date range, e.g. "2026-05-01 to 2026-05-31". Both endpoints are INCLUSIVE — the END day is covered (a 7-day range = 7 days). Quattr weeks run Sunday–Saturday; for "last week" use the most recent complete Sun–Sat span.
`granularity`	No	Time bucket size used to line the two metrics up: day, week (default) or month.	week

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 19, 2026

TDQS

A4.1/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already cover the safety profile (readOnlyHint=true, destructiveHint=false, openWorldHint=false), so the description's added value is the mechanics: it fetches each metric as a time series, aligns them by bucket, and returns correlation plus a significance value with an explicit causation caveat. It stops short of noting prerequisites such as minimum data points or behavior when a series is too short.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The framing question is front-loaded, followed by mechanism, examples and scope. The two illustrative examples cost some length but earn their place by clarifying cross-domain usage; the causation caveat is a useful closing note rather than filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description correctly compensates by describing the return payload (correlation coefficients plus significance and a caveat), and nested-object parameters are at least conceptually covered by the cross-domain framing. It is close to complete for a 5-parameter analytical tool, with only filter/segment semantics and edge-case behavior left implicit.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 60%, and the description reinforces the alignment concept ("lines them up by week (or day/month)") and the dual-method output, but the method and granularity enums are already documented in-schema. The nested metric_a/metric_b filters and segment fields get no explanation in the description, leaving part of the coverage gap unfilled.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a precise framing question and states the concrete operation: correlate two metrics from DIFFERENT domains over time. It names the statistical outputs (Pearson/Spearman with significance) and the domains it spans, which separates it cleanly from siblings like compare_periods or significance_check.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives a clear usage condition ("does X move with Y?" questions spanning search, web analytics, paid, market share, page experience) with worked examples. It does not, however, name an alternative tool or state when NOT to use correlation (e.g., when you want a plain period comparison), so the routing guidance is strong but not exhaustive.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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