data_data_anonymizer

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

With no annotations, the description carries the full burden and discloses that processing is client-side and mentions three masking modes. However, it does not clarify if the operation is read-only or destructive, nor does it define the substitution modes in detail.

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, front-loading the core purpose. The inclusion of 'Menu ID: data_anonymizer.' is redundant but not harmful. The reference to describe_tool could be seen as a deferral of detail.

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?

The description explains the input and processing but lacks details about the output format, error behavior, or limitations. Given the nested schema and no output schema, it is somewhat incomplete, though the client-side note adds important context.

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 0%, so the description must compensate. It lists PII types that correspond to the 'enable' object keys and mentions three modes for the 'mask' parameter, but does not define the exact format or allowed values for 'mask' beyond the three mode 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?

The description clearly states the tool anonymizes PII in text using specific modes (token, partial-redaction, counter substitution). It distinguishes itself from siblings like data_data_faker by focusing on masking rather than generating fake data.

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 use when needing to mask PII, but provides no explicit when-to-use or when-not-to-use guidance. It suggests using describe_tool for full guidance, which defers important context.

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