Quantified Self MCP Server

Overview Schema Source Related Servers Discussions Admin

Detect metric anomalies

detect_metric_anomalies

Read-onlyIdempotent

Flag days when a personal health metric deviates sharply from its own recent baseline, using a robust modified z-score to detect true outliers in noisy self-tracked data.

Instructions

Flag days where one metric deviated sharply from its own baseline over the window, using a median/MAD-based modified z-score rather than a mean/stdev z-score — more robust for short, noisy personal-health series, where the mean/stdev version is easily dragged around by the very outliers it's supposed to catch.

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.
`threshold`	No	Modified z-score cutoff. 3.5 (the default, Iglewicz & Hoaglin's standard value) flags only clear outliers; lower it (e.g. 2.5) to see more borderline days.
`start_date`	No	First day to include, formatted YYYY-MM-DD. Defaults to 90 days before end_date.

Output Schema

TableJSON Schema

Name	Required	Description
`range`	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.
`anomalies`	Yes
`threshold`	Yes

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",
-  "threshold",
-  "anomalies"
-]New value: +[
+  "metric",
+  "range",
+  "threshold",
+  "anomalies",
+  "evidence"
+]

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

TDQS

A4.1/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, so the safety profile is covered. The description adds valuable behavioral context: the statistical method (median/MAD modified z-score), the robustness rationale, and a privacy note about data being sent to the model. The privacy note is especially useful because it warns the agent that returned data may leave the local server, which is a behavioral trait not captured by annotations.

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 description is well-structured: the core purpose is front-loaded in the first sentence, followed by the statistical rationale and a privacy note. The privacy note is somewhat long but earns its place because it discloses a significant behavioral trait. No wasted words, though the statistical explanation could be slightly tighter.

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?

The tool has an output schema, so return values are already documented. The description covers the method, the rationale, and the privacy implication. It doesn't mention edge cases like what happens when there's insufficient data, but for a read-only anomaly detection tool with full schema coverage, this is a minor 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 description coverage is 100%, so the schema already documents all four parameters. The description adds context about the threshold (Iglewicz & Hoaglin's standard value) and the default window (90 days), but these are also partially in the schema. The description doesn't add much beyond what the schema provides, so a baseline 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?

The description states a specific verb ('Flag'), a specific resource ('days where one metric deviated sharply from its own baseline'), and a precise method (median/MAD-based modified z-score). It clearly distinguishes itself from sibling tools like get_baseline or calculate_metric_trend by focusing on anomaly detection rather than summary statistics or trend calculation.

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 explains why this tool exists (robust for short, noisy personal-health series) and why the mean/stdev version is inferior, giving an agent a clear sense of when to use it. It doesn't explicitly name sibling alternatives or state when not to use it, but the context is strong enough that an agent can infer it's for anomaly detection rather than general queries.

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

Deploy Server