df_get_measures

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds behavioral traits beyond those: automatic pagination and the ability to include generated SQL per measure via include_sql. This is useful context that is not in the annotations, so it earns a solid 4.

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 two concise sentences with no filler. The primary purpose and pagination are front-loaded, and the optional include_sql behavior is stated clearly in the second sentence. Every word earns 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?

For a read-only tool with auto-pagination, the description covers the essential behavior and the significance of the returned IDs for write tools. It lacks detail on the response structure, but given the absence of an output schema and the simplicity of the operation, this is acceptable. The remaining gaps are minor, so a 4 is warranted.

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 input schema describes project_id and version_id only, leaving language, use_cache, and include_sql without descriptions (40% coverage). The description compensates partially by explaining include_sql's effect, but it does not explain language or use_cache. Given the low schema coverage, the description should add more parameter detail; it does not fully compensate, so a 3 is appropriate.

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 states a specific verb ('Get all measures') and a specific resource ('of a project version'), and mentions automatic pagination. It is clear what the tool does, but it does not explicitly differentiate from sibling tools like df_get_facts or df_get_dimensions, though the resource name is distinct enough. This is clear but not explicitly contrasted with alternatives.

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 provides some usage context: it states that each measure carries a stable ID that write tools accept, implying this tool is used to obtain IDs for subsequent write operations. However, it does not explicitly state when to use this tool versus other read tools (e.g., df_get_facts) or when not to use it. The guidance is implicit rather than explicit.

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