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Metric trend

get_metric_trend

Read-onlyIdempotent

Compute the least-squares trend for a daily health metric series. Returns slope per day, endpoints, and spread to track changes over a specified period.

Instructions

Least-squares trend for one daily series: slope per day, endpoints, spread.

series is a name from list_metric_series. days selects the newest N days THAT HAVE DATA, not the last N calendar days — compare n_days with span_days to see whether the history is dense or sparse. since / until ("YYYY-MM-DD") fit a calendar window instead.

Returns arithmetic only. slope_per_day is in the series' own unit per day and carries no threshold, band or verdict; fewer than 3 days returns a null slope with a reason rather than a number fitted to noise.

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
`series`	Yes
`partner`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Changed5 schema fields changedv0.9.1Oct 3, 2026

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

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

A3.7/5.0

Behavior4/5

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

Annotations already carry the safe-read profile (readOnly, idempotent, non-destructive, closed-world), so the description's marginal value is its edge-case disclosure: arithmetic only, no threshold/band/verdict, and a null slope with a reason when fewer than 3 days exist. The coverage-block interpretation is useful but partially overlaps the output schema.

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?

Front-loaded with the core purpose, then parameter and output semantics in compact sentences. Dense but each sentence carries meaning; slightly long for a trend getter but no 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?

For a read-only trend tool with an output schema, the description supplies the edge-case and interpretation context an agent needs (density caveat, null-slope rule, coverage pairing). The gap is the two undocumented parameters, fresh and partner.

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 description coverage is 0%, so the description must compensate. It meaningfully explains series, days ('newest N days THAT HAVE DATA, not the last N calendar days') and since/until format, but leaves fresh and partner completely undocumented in both schema and description.

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

Purpose4/5

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

States a specific computation and resource: 'least-squares trend for one daily series: slope per day, endpoints, spread.' This clearly distinguishes it from a raw-value sibling like get_metric. It stops short of naming an alternative (compare_metric_periods, correlate_metric_series) to route against.

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

Usage Guidelines3/5

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

It gives condition semantics (series comes from list_metric_series; days vs since/until select different windows), which implies when each parameter applies. But there is no explicit when-to-use-this-vs-another-tool guidance, and no exclusions, so it remains implied rather than directive.

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