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Calculate metric trend

calculate_metric_trend

Read-onlyIdempotent

Fit a linear trend to any personal health metric over a date range, returning direction, daily slope, and r-squared to show how well the line fits.

Instructions

Fit a simple straight-line trend to one metric over a window and report its direction, slope (change per day), and r_squared (how well a straight line actually fits — low r_squared means "noisy," not "flat").

Privacy note: this server and its SQLite file are entirely local, but the data returned by this tool becomes part of the conversation sent to whatever model the calling client is configured with. If that model runs in the cloud rather than on your machine, treat this the same as pasting the data into a chat with that provider.

Input Schema

TableJSON Schema

Name	Required	Description
`metric`	Yes	One of steps, sleep_hours, resting_heart_rate, weight_kg, workout_minutes, mood, water_ml, heart_rate, hrv_ms.
`end_date`	No	Last day to include, formatted YYYY-MM-DD. Defaults to today.
`start_date`	No	First day to include, formatted YYYY-MM-DD. Defaults to 30 days before end_date.

Output Schema

TableJSON Schema

Name	Required	Description
`range`	Yes
`trend`	Yes
`metric`	Yes
`evidence`	Yes	Coverage/quality of the data a single-metric analytical result is based on. See evidence.build_evidence for how each field is computed.

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedSep 15, 2026

addedOutput schema / properties / evidence

Added value: +{
+  "description": "Coverage/quality of the data a single-metric analytical result is\nbased on. See evidence.build_evidence for how each field is computed.",
+  "properties": {
+    "confidence": {
+      "type": "string"
+    },
+    "coverage_ratio": {
+      "type": "number"
+    },
+    "expected_days": {
+      "type": "integer"
+    },
+    "freshness_days": {
+      "anyOf": [
+        {
+          "type": "integer"
+        },
+        {
+          "type": "null"
+        }
+      ],
+      "default": null
+    },
+    "gaps": {
+      "items": {
+        "properties": {
+          "days": {
+            "type": "integer"
+          },
+          "end": {
+            "type": "string"
+          },
+          "start": {
+            "type": "string"
+          }
+        },
+        "required": [
+          "start",
+          "end",
+          "days"
+        ],
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "measurement_count": {
+      "type": "integer"
+    },
+    "missing_days": {
+      "type": "integer"
+    },
+    "observed_days": {
+      "type": "integer"
+    },
+    "observed_end": {
+      "anyOf": [
+        {
+          "type": "string"
+        },
+        {
+          "type": "null"
+        }
+      ],
+      "default": null
+    },
+    "observed_start": {
+      "anyOf": [
+        {
+          "type": "string"
+        },
+        {
+          "type": "null"
+        }
+      ],
+      "default": null
+    },
+    "recent_gap_days": {
+      "type": "integer"
+    },
+    "requested_end": {
+      "type": "string"
+    },
+    "requested_start": {
+      "type": "string"
+    }
+  },
+  "required": [
+    "requested_start",
+    "requested_end",
+    "expected_days",
+    "observed_days",
+    "coverage_ratio",
+    "missing_days",
+    "measurement_count",
+    "gaps",
+    "recent_gap_days",
+    "confidence"
+  ],
+  "type": "object"
+}

changedOutput schema / required

Previous value: -[
-  "metric",
-  "range",
-  "trend"
-]New value: +[
+  "metric",
+  "range",
+  "trend",
+  "evidence"
+]

Addedv1.0.16Sep 15, 2026
Removedv1.0.15Sep 15, 2026
Addedv1.0.11Sep 13, 2026

TDQS

A4.2/5.0

Behavior4/5

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

Annotations already declare readOnlyHint and idempotentHint, covering the safety profile. The description adds meaningful context beyond annotations by explaining how to interpret r_squared ('noisy' vs 'flat') and disclosing a privacy consideration about data being sent to a cloud model.

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

Conciseness5/5

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

The core functionality is front-loaded in one concise sentence, followed by a clearly separated and relevant privacy note. Every sentence earns its place with no wasted words.

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, idempotent tool with an output schema covering return values, the description is nearly complete. It explains output interpretation and privacy implications. The only minor gap is the lack of explicit guidance on when to choose it over sibling analysis tools.

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 100%, so the schema already documents all three parameters. The description does not add extra parameter-level details beyond what the schema provides, so a baseline score of 3 is appropriate.

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?

States a specific verb and resource: 'Fit a simple straight-line trend to one metric over a window' and lists the exact outputs (direction, slope, r_squared). This clearly distinguishes it from sibling tools like detect_metric_anomalies or compare_metric_periods.

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?

The description implies its usage context: when you want a simple trend line for one metric over a window. It does not explicitly mention alternatives or exclusions, but the purpose is clear enough that an agent can decide when to choose it.

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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