Stats By Author

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

With no annotations, the description carries the full behavioral disclosure burden. It reveals a key behavioral trait: because authors is a free-text field, co-authored books form their own group rather than counting toward each author, and it notes 675,289 distinct author strings, indicating large result sets. This is valuable beyond what the schema states, though it doesn't address other behaviors like default sorting or read-only nature.

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 three sentences with no filler: purpose is stated first, followed by the two most important caveats. It is efficient and well-structured, with every sentence earning its place.

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 tool is complex with 9 parameters, but an output schema exists and the schema already documents parameters well. The description supplies the one non-obvious fact — the author field is a single string and co-authored works group separately — plus scale information. It doesn't cover when-to-use vs alternatives or general behavioral traits, but those are partially inferred from the schema and tool name.

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%, so baseline is 3. The description doesn't add parameter-specific semantics beyond what the schema provides; the schema already covers each parameter with rich detail, such as unit collapsing editions, order_by options, and min_books behavior. The grouping nuance contextualizes the author key but doesn't explain or augment any particular parameter.

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 author string, clearly identifying the resource and aggregation. It lacks a verb like 'returns' or 'computes', but the intent is unambiguous. It doesn't explicitly distinguish it from siblings, though the grouping unit is unique among the listed tools.

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 choose this tool over siblings such as stats_by_language or stats_by_year. The only contextual note is about the free-text authors field and co-authored grouping, which is interpretive rather than decision-oriented. This leaves the agent to infer usage from the tool name alone.

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