get_anomalies

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

Without annotations, the description effectively discloses the tool's behavior: it scans 12 anomaly types across 5 categories and returns results sorted by severity. It does not mention read-only nature, rate limits, or resource consumption, but covers the key behavioral traits of what the tool checks and orders.

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?

Description is front-loaded with the main purpose, then details categories, and ends with usage examples. It is concise and well-structured, though the list of categories could be slightly trimmed without losing meaning.

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 the complexity (5 parameters, no output schema), the description covers the input semantics and usage scenarios well. However, it omits details about the output format (e.g., what fields each anomaly includes), which would help the agent understand the return value. Without output schema, this gap reduces completeness.

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 description coverage is 100%, so the schema already documents all 5 parameters. The description adds context only by grouping anomaly types into categories and giving the default value for 'days' (180). This is moderate added value; 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?

Description clearly identifies the tool as detecting unusual patterns across NZX companies, lists 12 anomaly types across 5 categories, and distinguishes it from sibling tools that retrieve specific data points (e.g., get_insider_trades, get_governance_scores). It includes specific use case queries, making the purpose unambiguous.

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?

Description provides explicit usage guidance with example queries like 'any red flags?' and 'insider trading anomalies'. It states 'Use for...' which directly indicates when to use. However, it does not contrast with alternative tools or mention when not to use, missing some exclusion guidance.

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