meta_ads_split_tests_list

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

No annotations are provided, so the description carries the full burden. It explicitly states the tool is read-only, which is helpful. However, it does not disclose any other behavioral traits such as pagination behavior, rate limits, or required permissions beyond the implied ad account access.

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 sentences: the first explains purpose and outputs; the second provides usage guidance with sibling tools. It is concise, front-loaded, and every sentence adds value without redundancy.

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?

Given the lack of an output schema, the description adequately covers the output fields and provides the primary usage context (finding a study_id). However, it does not mention pagination behavior beyond the limit parameter, which would be helpful for complete understanding.

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 coverage is 100% with both parameters (account_id and limit) already described in the input schema. The description does not add additional meaning or context beyond what is in the schema, so it meets the baseline score of 3.

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 that the tool lists split tests, explicitly notes the internal API name (Studies), and names the fields returned (id, study_id, name, status, start_time, end_time, cells summary). It also distinguishes itself from sibling tools meta_ads_split_tests_get and meta_ads_split_tests_end.

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 says to use this tool to find a study_id before pulling detailed results via meta_ads_split_tests_get or ending via meta_ads_split_tests_end, providing explicit context for when to use it. It does not, however, mention when not to use it or alternative methods beyond the named siblings.

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