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

Annotations already declare readOnlyHint and idempotentHint, so the tool's safe, read-only nature is covered. The description adds value by disclosing the exact JSON return shape, including chapters and totals, which is especially useful because no output schema is provided.

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 compact and front-loaded, starting with the essential purpose, then the return structure, then usage guidance. Every sentence contributes meaningfully with no wasted 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 zero-parameter read-only tool, the description is complete: it states what the tool returns, the exact JSON shape, and when to call it. Since there is no output schema, including the return structure is especially valuable.

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 description explicitly confirms it takes no arguments. This meets the baseline for parameterless tools and leaves no ambiguity about invocation.

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 that the tool lists every chapter of a specific physics problem book with paragraph numbers and solved-problem counts, which distinguishes it from siblings like list_problems and list_paragraphs. The verb 'list' and the resource 'chapters' are 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 explicitly says to call this tool first to learn the shape of the corpus, giving clear contextual guidance. It does not explicitly describe when not to use it, but the 'call this first' instruction strongly implies its role relative to the sibling tools.

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