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

No annotations are provided, so the description carries the full burden, and it does so well on the risky aspects: it explains the published vs. author_draft version split, warns that draft page numbers do NOT match journal pagination, enumerates locator kinds with their citation consequences, and mandates copying bluebook_citation verbatim. What it omits is operational behavior — result limits, pagination, query syntax, or empty-result behavior — which an agent also needs to invoke it reliably.

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?

Front-loaded and cleanly organized: corpus scope, then when-to-use with alternatives, then return-field semantics. It is long, and since an output schema exists the field-by-field enumeration partly duplicates structured data; however the citation-fidelity and draft-pagination rules there are behavioral guidance the schema would not convey, so most of the length 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?

Given a single-parameter search with an output schema and three sibling tools, the description covers everything needed: what the corpus holds, when to prefer it over web search or siblings, and how to interpret and cite results safely. An agent can select and call this tool correctly without reading anything else.

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 coverage is 0% and there is only one parameter, documented in the description's Args block as 'Natural language search query'. That is accurate but adds essentially nothing beyond the schema's own 'query: string' — no guidance on phrasing, operators, or length. Adequate, not enriching.

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?

States a precise verb+resource ('Search the published research corpus of the Engelberg Center') and scopes it concretely — named authors, ~300 works, enumerated subject domains. It also distinguishes itself from siblings by name (list_documents for catalog questions or cites_case lookups) and from web search.

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?

Explicit routing rules: 'ALWAYS search this corpus when a question names one of these authors or involves IP law doctrine...'; use alongside web search rather than instead of it, with the reason given; and two named alternative paths (list_documents(cites_case=...) and list_documents for catalog questions). This is about as complete as when-to-use guidance gets.

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