Vaultbeat MCP Server

Overview Schema Source Related Servers Discussions Admin

Correlate two series

correlate_metric_series

Read-onlyIdempotent

Compute Pearson correlation between two daily metric series on overlapping recorded days, with an optional lag to test lead-lag relationships.

Instructions

Pearson r between two daily series, over days that have BOTH recorded.

lag_days pairs series_a on each day with series_b that many days LATER (negative = earlier): "does a short night show up in HRV the day after" is sleep vs HRV with lag_days=1. Sleep is already dated by the morning it ends, so lag 0 is "last night vs today". since / until ("YYYY-MM-DD") restrict to a calendar window.

Days missing on either side are dropped, never interpolated and never read as zero, so n_pairs is usually smaller than either series — quote it with r. Fewer than 3 shared days returns null with a reason: any two points are perfectly collinear, so a coefficient there is an artefact.

The result carries a caveat field about causation. Repeat its substance in your answer, and do not translate r into a word like "strong".

Carries a coverage block over the days the arithmetic used: quote coverage.days_covered and coverage.span_days beside any number here. coverage.window_satisfied: false alone does NOT mean a short history — it is also false when older days exist beyond the window; coverage.more_available tells the two apart.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`days`	No
`fresh`	No
`since`	No
`until`	No
`partner`	No
`lag_days`	No
`series_a`	Yes
`series_b`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Changed6 schema fields changedv0.9.1Oct 3, 2026

addedInput schema / additionalProperties
```
Added value: +false
```

addedInput schema / properties / lag_days

Added value: +{
+  "default": 0,
+  "title": "Lag Days",
+  "type": "integer"
+}

removedInput schema / properties / owner

Removed value: -{
-  "anyOf": [
-    {
-      "type": "string"
-    },
-    {
-      "type": "null"
-    }
-  ],
-  "default": null,
-  "title": "Owner"
-}

addedInput schema / properties / partner

Added value: +{
+  "default": false,
+  "title": "Partner",
+  "type": "boolean"
+}

addedInput schema / properties / since

Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "title": "Since"
+}

addedInput schema / properties / until

Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "title": "Until"
+}

First observedv0.8.1Sep 23, 2026

TDQS

A4.3/5.0

Behavior5/5

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

Annotations only declare the safety profile (read-only, idempotent, non-destructive); the description adds real behavioral depth: missing days are dropped rather than interpolated or zero-filled, n_pairs can be smaller than either series, <3 shared days returns null with a reason, and it explains the counterintuitive coverage.window_satisfied=false case. This is exactly the context annotations cannot carry.

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?

Dense and front-loaded: the definition comes first, then lag semantics, then edge cases and output guidance. Every paragraph does work, though the output-handling instruction ('repeat its substance... do not translate r into a word like strong') is a style directive that lengthens the definition beyond strict tool-selection needs.

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?

An output schema exists, yet the description still explains the two fields an agent must reason about (caveat, coverage) and warns against misuse of window_satisfied, which is genuinely useful. It is nearly complete; the unexplained days/fresh/partner parameters are the only real gap.

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 0%, so the description carries the full burden, and it does well for lag_days (including negative semantics and the 'sleep dated by the morning it ends' nuance), series_a/series_b pairing, and since/until format. But days, fresh, and partner are never mentioned, leaving three of eight parameters undocumented anywhere.

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?

Opens with a precise verb+resource: 'Pearson r between two daily series, over days that have BOTH recorded.' That is unambiguous and clearly distinct from siblings like get_metric_trend or compare_metric_periods, which measure trends/period shifts rather than pairwise association.

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?

Gives a concrete use case and the exact invocation for it ('does a short night show up in HRV the day after' = sleep vs HRV with lag_days=1), plus the condition that produces null (<3 shared days). It does not explicitly name a sibling alternative or state when NOT to use it, which keeps it short of a 5.

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