find_data_anomalies

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

With no annotations, the description partially carries the burden. It explains the behavior for each anomaly type (e.g., finds empty fields, duplicates), but does not disclose side effects, authorization needs, or resource impact. It implies read-only behavior but doesn't confirm.

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 short and well-structured, using a bullet list for anomaly types. It is efficient but could be more concise by integrating the types into a single sentence.

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 presence of an output schema and 8 parameters, the description covers the core detection logic but lacks context on authentication, filtering, and result interpretation. It is adequate but not fully complete.

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 50%, and the tool description adds value only for 'anomaly_type' by listing its options. Parameters like 'auth_user_id', 'scope_filter', 'auth_password', and 'data_center' are not elaborated in the description, leaving their semantics unclear.

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 detects data anomalies in SF entities and lists four specific anomaly types, making the purpose unambiguous. It distinguishes itself from sibling tools like 'audit_employee_data' by focusing on automated anomaly detection rather than manual audit.

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 lists the types of anomalies but does not provide explicit guidance on when to use this tool versus alternatives or any prerequisites. Usage is implied by the anomaly types, but no when-not-to-use or context is given.

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