get_profile_metrics

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

No annotations are provided, so the description carries full behavioral burden. It discloses return format (verbatim API response with dimensions and metrics), pagination behavior (all_pages, max_pages), that rates are not returned (must be derived), and network-specific metrics. This is comprehensive and transparent.

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 well-structured: a one-line summary followed by bullet points for each parameter, a return description, and a note. Every sentence adds value, and it is front-loaded with the purpose. It is concise yet comprehensive.

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 parameters are fully explained, an output schema exists (so return values need not be detailed), and the description includes a key note about derived rates, the description is complete for an agent to invoke this tool correctly.

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?

Despite 0% schema description coverage, the description explains all 7 parameters in detail: types, constraints, examples, defaults, and relationships to other tools (list_profiles, list_metrics). This adds significant value beyond the schema alone.

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's purpose: 'Pull profile-level analytics for a date range (daily granularity).' It identifies the verb and resource, and the sibling tools are distinct (e.g., list_profiles, get_post_performance), so there is no ambiguity.

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 provides strong usage context, including how to obtain profile_ids from list_profiles, how to find valid metric keys via list_metrics, and constraints like max 100 IDs and date range <=1 year. It does not explicitly state when NOT to use this tool (e.g., for individual post performance, use get_post_performance), but the context of sibling tools and the detailed parameter guidance make it clear.

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