Gizmo Analytics

detect_anomalies

Read-onlyIdempotent

Identify days with abnormally high or low traffic using statistical z-score analysis across sites. Flags anomalies based on configurable thresholds to reveal unexpected traffic patterns.

Instructions

Find unusually high or low traffic days across sites using per-site z-score. For each site, computes the mean and stddev of daily visitors over the range, then flags any day where |z| ≥ threshold (default 2.5, ~1-in-100). Use for 'anything weird?', 'spikes this week', 'show me unusual traffic'. Pass site_id to scope to one site, otherwise scans the whole fleet. Returns most-recent anomalies first. Sites under 20 visitors total are skipped (not enough signal).

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "detect_anomalies"
  }
}

Input Schema

Name	Required	Description
`site_id`	No	Internal site UUID. Get one from list_sites. Omit to scope to the entire workspace.
`date_range`	No	Time window. One of: today, yesterday, last_7_days, last_14_days, last_30_days, last_90_days. Defaults to last_14_days for stable mean/stddev.
`threshold`	No	Z-score absolute value above which to flag a day as anomalous. Defaults to 2.5 (~1-in-100). Lower = more sensitive.

Output Schema

Name	Required	Description	Default
`range`	Yes
`threshold`	Yes
`sites_checked`	Yes
`anomalies`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Jun 20, 2026

TDQS

A4.8/5.0

Behavior5/5

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

Consistent with annotations (readOnlyHint, no destructiveness). Adds behavioral details: threshold default, skipping low-traffic sites, returning most-recent first. No contradictions.

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?

Single paragraph with no wasted sentences. Each sentence adds unique value: function, method, use cases, scoping, ordering, threshold, edge case. Concise yet comprehensive.

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

Completeness5/5

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

With 100% schema coverage, rich annotations, and an output schema, the description covers purpose, method, parameters, and edge cases completely. No gaps.

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

Parameters5/5

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

Adds meaningful context beyond schema: site_id omission scopes to workspace, date_range default rationale, threshold default and sensitivity meaning. Schema coverage is 100%, but description enriches understanding.

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 clearly states it finds unusually high or low traffic days using per-site z-score, with explicit examples like 'spikes this week'. It distinguishes itself from sibling analytics tools by focusing on anomaly detection.

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?

Provides explicit use cases ('anything weird?', 'spikes this week') and explains scoping to a single site or entire fleet. Lacks explicit when-not-to-use, but overall clear context.

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