diff_diff_stats

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

No annotations are provided, so the description carries full burden. It does not disclose behavioral traits such as read-only nature, performance implications, or side effects. While it describes what is returned, it omits important context for a compute-intensive tool.

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 a single sentence plus return type spec. It is front-loaded with the purpose and contains no fluff. Every word adds value.

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?

There is an output schema, but the description explicitly lists return fields, which is helpful. However, it lacks edge-case handling (e.g., empty strings, very large texts) and no usage context. Adequate for a simple tool but not 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?

The schema has 0% description coverage, so the description must compensate. However, it only says 'two texts' without detailing text_a and text_b (e.g., format, length limits, encoding). This adds minimal value beyond parameter 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 it computes similarity and change statistics between two texts, and explicitly lists the return fields (similarity, added_chars, etc.). It distinguishes itself from sibling tools like diff_diff_text and diff_diff_similar by focusing on statistics.

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 is for computing diff stats, but does not provide explicit guidance on when to use this tool versus alternatives such as diff_diff_text or diff_diff_similar. No exclusions or when-not-to-use are mentioned.

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