mgnify_list_super_studies

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

With no annotations, the description carries the full burden, but it only says 'List' without indicating side effects, permissions, or whether it is strictly read-only. It also does not mention any rate limits or data scope.

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 a single sentence with no unnecessary words. It is well-structured, though it could benefit from a brief note on pagination params, but brevity is a strength here.

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?

The description explains what super-studies are (umbrella collections) and gives examples, but it does not clarify how this list differs from listing regular studies or when to fetch a single super-study. Given the context of sibling tools, some guidance would help.

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 includes a 'params' object with 'page' and 'size', but the description does not mention these parameters at all. Since schema coverage is 0%, the description adds no meaning beyond 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?

The description clearly states the action ('List') and the resource ('super-studies'), and provides concrete examples (Tara Oceans, Earth Microbiome Project, HoloFood) that distinguish it from other list tools like mgnify_list_studies.

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 gives no explicit guidance on when to use this tool versus siblings such as mgnify_list_studies or mgnify_get_super_study. It does not mention typical use cases, pagination behavior, or how to narrow results.

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