meta_ads_analysis_audience

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 states 'Read-only,' which is a key behavioral trait, and describes the output shape: rows per age_range × gender with spend/conversions/CPA and recommendations array with example. It doesn't mention potential data-quality caveats or that it may return empty results for small campaigns, but the read-only declaration and output details provide solid transparency.

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 tight and well-structured: it opens with the core purpose, lists the output metrics and recommendations example, states read-only status, and ends with usage guidance and a named alternative. Every sentence adds value; no redundant or vague wording.

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 analysis tool with no output schema, the description explains what the return data contains (rows, fields, relative_score, recommendations) and includes a concrete example. It also contextualizes when to use it (before targeting adjustments). It could mention limitations (e.g., requires enough delivery data) but is complete enough for typical selection and invocation.

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 has 100% description coverage for all three parameters, including period formats, account_id fallback, and required campaign_id. The description adds no additional parameter-specific semantics beyond what the schema already provides. Baseline of 3 is appropriate since the schema fully documents parameters.

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?

Description clearly states the tool 'Scores delivery efficiency across age × gender segments and flags the best and worst performing buckets.' It names the specific analysis dimension (age×gender), the comparison metric (relative_score vs campaign average), and produces a recommendation array. This is distinct from sibling tools like meta_ads_insights_breakdown, which provides raw numbers, and other analysis tools.

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?

Explicit guidance is given: 'Use before adjusting targeting' and directly names the alternative 'for raw breakdown numbers use meta_ads_insights_breakdown with breakdown="age,gender".' This clearly tells the agent when to choose this tool and when to choose another.

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