meta_get_study_results

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

With no annotations provided, the description carries the full burden. It does disclose the return content ('objectives, cells, and lift results') and the 'Get' wording implies a read-only operation. However, it does not mention whether results require a completed study, authentication prerequisites, or any availability constraints.

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?

Two concise sentences with no filler. The primary action is front-loaded and the return contents are stated efficiently. Every sentence 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 simple one-parameter getter with no output schema, the description is reasonably complete: it names the resource, the action, and the key return values. It would be more complete if it noted the relationship to meta_get_ad_studies or meta_interpret_experiment_results, but the core invocation context is clear.

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 already fully describes studyId as 'The Ad Study ID' with 100% coverage, so the description adds only the context that the study is a conversion lift or A/B test. This is adequate but does not provide extra parameter-level 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 tool gets detailed results for a conversion lift or A/B test study and lists the returned components (objectives, cells, lift results). It distinguishes itself from list-style tools like meta_get_ad_studies, but it does not explicitly differentiate from the closely related meta_interpret_experiment_results sibling.

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 guidance about when to choose this tool instead of alternatives such as meta_get_ad_studies for listing studies or meta_interpret_experiment_results for interpreting results. The only implied usage is 'when you need detailed results,' which largely restates the tool's purpose.

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