disclosure_anomaly

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

No annotations are provided, so the description bears full burden for behavioral disclosure. It explains that the tool returns a structured score, flags, and evidence without making direct recommendations. However, it does not mention side effects, read-only nature, rate limits, or error handling, leaving some transparency gaps.

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?

The description is concise, with three sentences covering purpose, output, and a note on LLM use. It front-loads the key information without wasted words, though a more structured format could enhance readability.

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?

With no output schema, the description partially covers the return format (score, flags, evidence, data frame) but lacks detail on the structure of flags and evidence. Given the tool's complexity (multiple factors), more completeness would be beneficial.

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%, with each parameter having a description in the schema. The tool description does not add significant meaning beyond the schema, as it focuses on the output rather than parameter details. 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 clearly states the tool computes an accounting/governance anomaly score based on several factors (correction disclosure ratio, auditor change, etc.) and returns a score 0-100 with flags and evidence. It distinguishes itself from sibling tools like 'get_financials' by focusing on anomaly detection rather than raw data retrieval, though sibling differentiation is not explicit.

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 analyzing disclosure anomalies and notes the tool does not make direct recommendations, leaving judgment to the LLM. However, it does not explicitly state when to use it versus alternatives or provide conditions for use, leaving guidance implicit.

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