anonymize_structured_data

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions that the tool anonymizes PII, but doesn't explain what happens during anonymization (e.g., whether data is modified in-place, if original data is preserved, error handling, or performance considerations). For a mutation tool with zero annotation coverage, this is a significant gap in transparency.

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 appropriately sized and well-structured: it starts with a clear purpose statement, followed by an 'Args' section listing parameters with defaults, and ends with a 'Returns' section. Each sentence earns its place, but it could be more front-loaded by emphasizing the tool's role relative to siblings upfront.

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 tool's complexity (5 parameters, mutation operation, no annotations) and the presence of an output schema (which covers return values), the description is moderately complete. It explains parameters and returns, but lacks behavioral context (e.g., how anonymization works) and usage guidelines, making it adequate but with clear gaps for effective agent use.

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 0%, so the description must compensate. It lists all 5 parameters with brief explanations (e.g., 'JSON string representing structured data' for 'data'), which adds meaning beyond the bare schema. However, it doesn't provide details on allowed values (e.g., valid 'language' codes, 'operator' options, or 'entities' types), leaving gaps in parameter understanding.

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's purpose: 'Anonymize PII in structured data (JSON/dict).' It specifies the verb ('anonymize'), resource ('structured data'), and format ('JSON/dict'), which is specific and actionable. However, it doesn't explicitly differentiate from sibling tools like 'anonymize_text' or 'batch_anonymize', which handle similar anonymization tasks but for different data types or in batch mode.

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 provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'anonymize_text' (for text data) or 'batch_anonymize' (for batch processing), nor does it specify prerequisites, exclusions, or typical use cases. This leaves the agent without context for tool selection among similar anonymization options.

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