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

With no annotations, the description carries the full burden and does so thoroughly. It discloses the exact return contents (verbatim definition, chapter, other books, claim, related terms, links block) and clearly describes the fallback behavior for unknown terms.

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 longer than minimal but every sentence provides useful information: identity, usage triggers, return structure, and unknown-term fallback. The list of example terms is slightly expansive but serves to disambiguate the tool's domain.

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 single parameter and no output schema, the description covers everything an agent needs: what to pass, what to expect back, and what happens for unknown terms. The tool is simple and the description fully equips the agent to select and invoke it correctly.

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 already documents the single 'term' parameter at 100% coverage, so the baseline is 3. The description adds meaningful value by clarifying that the parameter accepts a term, alias, or slug and providing concrete examples, earning a 4.

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 the tool's function: it defines terms coined or pinned down by Greenlit books, with specific examples. It is distinguishable from siblings like get_concept through its focus on glossary definitions, but it does not explicitly name or contrast any sibling tool.

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?

Provides explicit use cases: 'when someone asks what a term means, asks you to define it, or uses it and you want the source.' This gives clear context for when to invoke the tool, but it does not mention alternatives or state when not to use it.

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