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

Annotations already declare readOnlyHint=true, so safety is covered; the description adds that it is a GET endpoint and discloses the response shape (field type, unique-value count, topValues [{value, studiesCount}]). It omits things like how many topValues are returned or pagination behavior, so not a full 5.

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 short sentences, zero filler, front-loaded with the purpose before the return format and usage hint. Every sentence carries distinct information.

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, but the description compensates by outlining the returned fields, which is exactly what an agent needs to consume the result. A minor gap remains around result size/ordering limits, but the core need is met.

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%, so both parameters are already documented with examples and enum types. The description does not add syntax or semantics beyond what the schema provides (it never even mentions the 'types' filter), so baseline 3 applies.

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 ('Discover valid values / enums for study fields') and immediately clarifies the return shape. It is clearly distinguishable from siblings like get_studies_stats and search_studies 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 a concrete usage context ('finding valid enum values ... before building a filter'), which tells the agent when this tool is the right prerequisite step. It does not explicitly name an alternative tool or state when not to use it, so it stops short of a 5.

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