dataset_info

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

No annotations are supplied, so the description carries the full burden, and it discloses little: it names categories of data (pathology, study type, contrast phase, windows, presets, option names) but says nothing about read-only safety, return structure, or how that content is organized. For a lookup/metadata tool with no output schema, that leaves the response shape entirely undisclosed.

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?

It is a single short clause with no padding, so length is not a problem. However, it is a grammatically incomplete fragment that is not front-loaded with a purpose statement, so brevity comes at the cost of clarity.

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 no annotations, no output schema, and no parameters, the description is the only source of information about this tool, and it does not say what calling it produces, in what form, or when it should be preferred over the list_*/find_* siblings. The gap is significant relative to how little structured metadata is available.

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 tool takes zero parameters, so there is nothing for the description to disambiguate; the baseline for a parameterless tool applies. The listed content categories describe what the call covers rather than argument meaning.

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 is a bare noun phrase ('Filter values..., windows, presets, and option names') with no verb and no statement of what the tool actually does or returns. It hints at dataset metadata enumeration, but an agent cannot tell whether this returns available filter values, a dataset summary, or configuration options, nor how it differs from siblings like list_structures or list_findings.

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?

There is no when-to-use or when-not-to-use guidance and no mention of any alternative tool. At best the agent can infer 'call this to discover filter values before searching,' but that inference is never stated and no sibling is referenced.

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