meta_interpret_experiment_results

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

Annotations are absent, so the description carries the full burden of behavioral disclosure. It states that the tool 'reads and interprets' results, suggesting a read-like operation, but it does not explain what 'confidence guardrails' actually do, whether interpretation involves any transformation or side effects, how output is structured, or any rate-limit or permission considerations. Critical behavioral context is missing.

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 a single, front-loaded sentence that introduces the tool's core purpose and key concepts without filler. It could earn a 5 if it also included a brief usage pointer, but as written it is appropriately concise and scannable.

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 tool with no annotations, no output schema, and a 60% schema-covered parameter set, the description is too thin. It leaves unclear how the tool differs from meta_get_study_results, how the confidence guardrails are computed or applied, what the optional cell entities affect, and what an agent should expect in the response. A competent agent would likely need to inspect the tool implementation or call it speculatively to understand its behavior.

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 60%, with studyId, adAccountId, and includeCellEntities documented but limit and cellEntityType left undocumented. The description mentions 'optional cell entities,' which partially relates to includeCellEntities and cellEntityType, but it does not clarify the relationship between adAccountId and studyId, the meaning of cellEntityType, or the limit parameter. It adds only marginal value 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 uses a specific verb ('Read and interpret') and names the resource ('A/B test or conversion lift study results'), with additional qualifiers (confidence guardrails, cells, objectives, optional cell entities) that distinguish it from a simple result fetcher like meta_get_study_results. It does not explicitly contrast itself with sibling tools, but the 'interpret' language and study-analysis framing make the purpose reasonably clear.

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?

No guidance is given about when to use this tool versus alternatives such as meta_get_study_results or meta_get_ad_studies. It does not state prerequisites, whether studyId should be preferred over adAccountId, or when to set includeCellEntities. The usage context must be inferred from the parameter schema rather than explicitly explained.

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