detect_transaction_outliers

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

With no annotations, the description carries the burden of behavioral disclosure. It does well by explaining the z-score methodology and the exact flagging rule (|z| ≥ threshold). It could also mention whether the operation is read-only and what the return payload looks like, but the core behavior is transparent enough for a detection tool.

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

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

Two sentences with no filler: the first front-loads the method and scope, the second states the criterion and use cases. Every phrase contributes to understanding the tool.

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

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

The tool has no output schema, and the description does not state what the tool returns (e.g., a list of flagged transactions, a summary count, or scores). While the parameters are fully described and invocation is straightforward, the missing return semantics leaves a notable gap for an agent trying to use the result.

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

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

The input schema already documents all three parameters with 100% coverage, including defaults and value formats. The description does not add significant parameter-level meaning beyond the schema; it only echoes AR/AP and threshold concepts. Baseline 3 is appropriate.

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

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

The description names a specific statistical method (z-score analysis), a specific resource (AR/AP invoice amounts), and a specific outcome (flagging transactions with |z| above threshold). This clearly distinguishes it from sibling detection tools like detect_customer_churn or detect_seasonality, which target different resources.

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

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

It gives explicit application contexts: fraud detection, data-entry errors, and pricing anomalies, and it clearly scopes the tool to AR/AP invoice transaction amounts. However, it does not name alternative tools or state when not to use it, so it stops short of a 5.

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