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 discloses that the tool is read-only, pulls data live from Meta, and that the API cannot create experiments. This is strong behavioral context for a list tool, though pagination and rate-limit behavior are not mentioned.

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 dense sentences with no filler. The purpose and resource are front-loaded, and every sentence adds meaningful information about scope, usage, or behavioral constraints.

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 enumerates key returned fields (type, status timestamps, description) and clarifies the data source. The parameters are fully documented in the schema. Slightly more detail about pagination and defaults in the description would improve completeness, but the schema already covers it.

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 each parameter already documented in the input schema. The description adds no parameter-specific meaning beyond what the schema provides, so the baseline of 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 the specific verb 'List' and resource 'Meta experiments / ad studies', and enumerates the included data (A/B split tests, conversion-lift studies, type, status timestamps, description). This clearly distinguishes it from sibling list_* tools, none of which target Meta experiments.

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?

Explicitly states when to use it: to audit which experiments exist or ran before analyzing results in Ads Manager. It also clarifies that creation is not supported via the public API and must happen in Meta Ads Manager, effectively ruling out use for setup. However, it does not name a specific sibling alternative.

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