diff_anonymization

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

No annotations provided, so description carries burden. It describes the output (plain unified-diff string) but does not disclose side effects, permissions, or error conditions. It's adequate but minimal.

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?

Three sentences front-load the main purpose, parameter descriptions are compact, and every sentence adds value. No unnecessary words.

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 simple diff tool with 2 parameters and an output schema, the description covers inputs, output format, and intended use. Could mention edge cases like empty strings or large texts, but overall sufficient.

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 has 0% description coverage, so description adds meaning for both parameters: 'Original (pre-anonymization) text' and 'Anonymized text from anonymize_text()'. This clarifies the expectation beyond bare string type.

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 returns a unified diff comparing original and anonymized text, with specific verb 'Return' and resource 'unified diff'. It distinguishes itself from sibling tool anonymize_text by being a complementary diff operation.

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 it should be used after anonymize_text by referencing 'from anonymize_text()'. No explicit when-not or alternatives, but the context is clear enough for this simple comparison tool.

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