Dataset Overview

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

With no annotations provided, the description carries the full burden, and it handles this well. It discloses that the tool reports live counts, focuses specifically on columns with coverage problems, returns a full caveat list, and includes three defects not mentioned in DATA_NOTES.md. This gives the agent useful expectations about the tool's behavior and output beyond the tool name.

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 wasted words. The core purpose is front-loaded in the first sentence, the usage directive appears second, and the added value of the tool's specific reporting behavior is in the third. Every sentence earns 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?

For a zero-parameter tool with an output schema, the description is complete. It explains why the tool exists, when to call it, and what it returns. The presence of an output schema means the description does not need to enumerate return fields. No critical context appears missing for an agent deciding to invoke it.

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 tool has zero parameters and the schema coverage is 100%, so there is no parameter information for the description to add. The baseline for zero-parameter tools is 4, and the description appropriately focuses on the tool's purpose and output rather than parameter details.

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 a clear purpose: report the shape, coverage, and known defects of the Goodreads dataset. It goes beyond a vague overview by specifying that it reports live row/population counts for columns with coverage problems and returns a full caveat list. This distinguishes it from the sibling tools, which are focused on ratings, statistics, and distributions rather than dataset-level quality.

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 gives an explicit usage directive: "Call this before answering anything substantive." This tells an agent when to invoke the tool. It does not explicitly name alternatives or exclusions, but the instruction to call it first provides clear context for when it should be used.

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