operational-data-mcp

Find Anomalies

find_anomalies

Identify anomalies in production counts by applying z-score detection. Returns timestamps where an asset's hourly count exceeds a specified threshold from its historical mean.

Instructions

Run simple z-score anomaly detection on production counts. Returns timestamps where an asset's hourly count is more than threshold standard deviations away from its mean.

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": "find_anomalies"
  }
}

Input Schema

Name	Required	Description	Default
`asset_id`	No	Limit to one asset; otherwise checks all assets
`threshold`	No	Z-score threshold (default 2.0)

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 17, 2026

TDQS

A3.9/5.0

Behavior3/5

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

Since no annotations are provided, the description carries the burden. It adds context about the algorithm (z-score, standard deviations from mean) and the meaning of the threshold, but does not explicitly state whether the operation is read-only, how it handles missing asset_id (all assets), or any side effects. It also does not clarify the full return structure beyond timestamps.

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 description is two sentences long and immediately states the tool's purpose and output. There is no redundancy or irrelevant information; every word contributes to understanding.

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

Completeness3/5

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

The tool is relatively simple with 2 optional parameters and no output schema. The description explains the algorithm and that timestamps are returned, but it does not specify the full return format (e.g., whether it includes the anomaly score or value) or the scope when asset_id is omitted. This is a gap given the absence of an output schema, but the description is otherwise adequate.

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

Parameters4/5

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

Schema coverage is 100%, so baseline is 3. The description adds meaningful context by explaining the threshold's role ('more than threshold standard deviations away from its mean') and introduces the concept of 'hourly count,' which is not in the schema. This adds value beyond the schema's parameter descriptions.

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 a specific action ('Run simple z-score anomaly detection') on a specific resource ('production counts'), and explains the output ('Returns timestamps'). This distinguishes it from sibling tools like list_assets or query_production, which serve different purposes.

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?

The description implies when to use the tool (when you need to detect anomalies in production counts), but does not explicitly state when not to use it or mention alternative tools like query_production. There are no exclusions or comparisons, so guidance is only implicit.

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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