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ai_anomaly_detection__iqr_anomalies

Identify outliers in financial datasets using the interquartile range method. Detect anomalies by flagging values beyond a specified multiple of the IQR.

Instructions

[ai-anomaly-detection] iqr_anomalies

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kNo
valuesYes
Behavior1/5

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

The description does not disclose any behavioral traits. Since no annotations are provided, the description carries the full burden, but it fails to mention any side effects, assumptions, or data requirements. It is essentially empty.

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

Conciseness2/5

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

While extremely short, it is not concise in a meaningful way; it omits necessary information. Every sentence of the description should add value, but this one merely repeats the name, making it insufficient rather than concise.

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

Completeness1/5

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

The tool is a statistical anomaly detection method with two sibling tools and a total of two parameters. The description provides no context about input requirements, output format, or how it differs from siblings. It is completely incomplete.

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

Parameters1/5

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

The input schema has two parameters (values, k) with 0% description coverage, and the description adds no explanation. The agent has no clue what values should contain (e.g., numerical array) or what k represents (e.g., IQR multiplier).

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

Purpose1/5

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

The description only repeats the tool name with a prefix '[ai-anomaly-detection] iqr_anomalies'. It lacks any verb or resource indicating what the tool does, e.g., 'Detects anomalies using IQR method'. Without context, an agent cannot infer the purpose.

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

Usage Guidelines2/5

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

No guidance is provided on when to use this tool versus sibling anomaly detection tools like rolling_deviation_anomalies or zscore_anomalies. The description offers no usage context or alternatives.

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

Install Server

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