disclosure_anomaly

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

No annotations provided, so description carries full burden. It explains what the tool computes (score, flags, evidence) and that it does not give recommendations. It does not disclose data freshness, rate limits, or side effects of calling the 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?

Description is a single sentence succinctly listing what the tool does and returns, with no wasted words. It is front-loaded with the key concept and then details.

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?

For a tool with no output schema, the description adequately explains the output (score, flags, evidence, data frame). Parameters are fully covered. Sibling tools exist but not explicitly compared. The description is fairly complete for the tool's purpose.

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 4 parameters. The added description explains the composite nature of the score and the purpose of audit_years for comparing auditor/opinion, providing meaning beyond schema parameter names.

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 states the tool computes a score (0-100) for accounting/governance anomaly signs including restatement ratio, auditor change, adverse audit opinion, capital stress, and returns structured flags and evidence. This distinguishes it from sibling tools like buffett_quality_snapshot or insider_signal.

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 implies use for anomaly detection and provides a data frame for LLM judgment without direct recommendations. However, it lacks explicit when-to-use, when-not-to-use, or mention of alternative tools among siblings.

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