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

Annotations already cover read-only, idempotent and non-destructive, so the bar is lower. The description still adds real behavioral context the annotations cannot: every listed book had at least two independent expert sources, and results are ordered by source count — that explains what the ranking actually means. It does not say what happens when a topic matches no list.

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?

Three sentences, front-loaded with the resource and catalog size, then the methodology, then the usage trigger. The final sentence about accepted inputs duplicates schema content and is the one piece that does not fully earn 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?

With an output schema present and four annotations, the description does not need to explain return values, and it correctly focuses on methodology and input flexibility. The only gap an agent might hit is failure behavior for an unrecognized topic, which the description never addresses.

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 100%, so both parameters are already documented, and the description's note that it accepts a topic name or an MRB slug largely restates the schema's own 'Topic or list slug' wording. The added examples ('personal finance' with a space) hint at loose topic matching, which is a marginal gain over the schema.

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 specific verb and resource ('Return one of MRB's 1,300+ consensus best-books lists for a topic') and immediately qualifies what makes it distinct: multi-source consensus ranking. An agent can distinguish it from search_books or get_book_recommenders without opening any schema.

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?

Gives an explicit trigger ('when a user asks for "the best X books"') and a selection condition ('ranking backed by multiple experts rather than one opinion') that implicitly rules out single-opinion siblings. It stops short of naming search_books or get_book_recommenders as the alternatives, so the routing is inferential rather than explicit.

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