score_outliers

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

With no annotations, the description carries the transparency burden. It discloses the methods (Z-score, IQR) and outputs but does not mention side effects (read-only), auth needs, rate limits, or handling of missing data. Adequate but not comprehensive.

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, each adding value. First sentence states action and method, second lists outputs. No redundancy or fluff.

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?

Given no output schema, the description mentions return types (risk flags, summary) but lacks specifics on structure, error handling, or performance considerations. Acceptable but not fully complete for a 3-param tool.

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?

Schema coverage is 100% with descriptions for all parameters. The description adds context that numeric_fields are the fields to analyze but does not provide significant new meaning beyond the schema. Baseline score of 3 applies.

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 clearly states the tool scores records for outlier risk using Z-score and IQR methods on specified numeric fields, and returns per-record flags and a summary. This clearly distinguishes it from siblings like audit_data_quality or detect_bias.

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?

The description implies usage for outlier detection in financial datasets but does not explicitly state when to use it vs. alternatives (e.g., detect_bias for fairness, audit_data_quality for general checks). No when-not-to-use guidance.

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