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

Annotations already declare readOnlyHint=true and openWorldHint=false, so safety is covered. The description goes further by disclosing what each result contains (purpose, licence, whether it runs online on Corollary with no GPU or installation, and the page URL), which is genuine behavioural context an agent cannot get from the annotations or schema. It does not mention ranking or result-count behaviour.

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 with the catalogue scope, then the result shape, then the usage trigger — a logical order with no filler sentences. It is longer than strictly necessary: the long parenthetical domain lists (protein structure prediction, protein design, docking, antibodies, genomics...) and tool/database name examples could be trimmed, though they do aid query formulation.

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?

There is no output schema, so the description commendably fills that gap by describing what each result reports (function, licence, online-runnability, page URL). Combined with the readOnly annotation, an agent has enough to call it correctly; only ranking/ordering and what happens on zero results remain unspecified.

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 description coverage is 100%, including an enum for 'kind' and an example-laden query description, so the schema carries the parameter semantics. The description's domain enumeration ('protein design, docking, genomics, single-cell, chemistry/ADMET' etc.) hints at useful query terms but adds no format or filtering guidance beyond the schema. Baseline 3 is correct here.

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 (search) and a precisely scoped resource (Corollary's catalogue of open scientific AI models, bioinformatics programs, databases, nf-core pipelines and datasets). The enumerated domains make it immediately distinguishable from siblings like compare_scientific_ai_for_task and get_scientific_ai_details 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?

Explicitly states when to use it: 'when a user asks which model or tool to use for a biology or chemistry task, or where to run one.' It gives a clear situational trigger but never names the adjacent alternatives (compare_scientific_ai_for_task, get_scientific_ai_details) or says when to prefer those over a plain search, so routing between siblings still requires inference.

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