batch_anonymize

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 states the tool returns a 'JSON string with anonymized results for each text,' which gives basic output info, but lacks critical details: it doesn't specify what 'anonymize' entails (e.g., redaction, masking, pseudonymization), whether it's a read-only or mutating operation, potential rate limits, error handling, or privacy implications. For a tool with no annotations and 5 parameters, 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 well-structured and appropriately sized: it starts with a clear purpose statement, followed by a bullet-point list of parameters and returns. Each sentence earns its place by conveying essential information without redundancy. It could be slightly more front-loaded by integrating parameter defaults into the initial statement, but overall it's efficient and readable.

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 complexity (5 parameters, no annotations, but has an output schema), the description is moderately complete. It covers parameters and output format, but lacks behavioral context (e.g., how anonymization works, side effects) and usage guidelines. The output schema existence reduces the need to detail return values, but without annotations, the description should do more to explain the tool's operational traits and constraints.

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?

The description lists all 5 parameters with brief explanations, adding meaning beyond the input schema, which has 0% description coverage. For example, it clarifies that 'entities' defaults to 'all' and 'score_threshold' is a 'minimum confidence score,' which the schema only titles generically. However, it doesn't elaborate on allowed values (e.g., what 'operator' options exist) or provide examples, preventing a perfect score.

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 multiple texts in batch.' It specifies the verb ('anonymize'), resource ('multiple texts'), and scope ('in batch'), which distinguishes it from sibling tools like 'anonymize_text' (likely single-text) and 'anonymize_structured_data' (different resource type). However, it doesn't explicitly contrast with 'batch_analyze' or other batch operations, keeping it from a perfect score.

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 single texts, 'anonymize_structured_data' for non-text data, or 'batch_analyze' for analysis versus anonymization. Without such context, an agent must infer usage from tool names alone, which is insufficient for clear decision-making.

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