Stats By Language

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

With no annotations, the description bears the transparency burden and handles it well by disclosing that grouping uses language_normalised rather than the raw language column. It also highlights the severe data-coverage limitation: only 13.6% of books have a language label and 83% of those are English. These are important behavioral/statistical pitfalls that an agent would not know from the schema alone.

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 short, front-loaded with the core purpose, and every sentence adds value: the grouping rule, the raw-column warning, and the coverage caveat. There is no filler or redundant restating of the tool name.

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 a fully documented eight-parameter schema and an output schema, the description is largely complete for safe and correct use. It adds the two most critical caveats, normalization and sparse coverage, that the schema and annotations cannot express. Only explicit sibling routing is missing, but that gap is already captured in usage_guidelines.

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 100% and each parameter already has detailed semantics, so the baseline is 3. The description adds no parameter-specific meaning beyond the schema, but none is needed here.

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 states that this tool returns rating statistics grouped by language, which is a clear and specific function. It does not explicitly contrast with sibling stats_by_year, stats_by_publisher, or stats_by_author, but the language focus is unambiguous.

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 this is the tool for language-based statistics and includes a strong caveat about data coverage before quoting results. However, it does not explicitly say when to choose this over sibling statistics tools or when an alternative would be more appropriate.

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