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

With no annotations provided, the description carries the full burden. It discloses cache-only behavior, legacy common-wire surface mapping adset to TikTok's native ad group role, attribution limitations, daily mode behavior, and metadata semantics. It doesn't mention pagination limits or error behavior, but the disclosed traits are substantial and go well beyond a basic summary.

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 dense but well-structured: a summary sentence, a cache/legacy note, attribution and mode semantics, then REQUIRED/DATE/EXAMPLE blocks. It front-loads the core purpose and uses formatting to make requirements scannable. Slightly long, but every sentence adds value and the structured callouts justify the length.

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 16-parameter tool with no output schema and no annotations, the description covers the essential invocation contract: required fields, date parameter naming, scope filter, attribution constraints, and an example. It doesn't explain the meaning of consistency, require_complete_range, or query_contract_version, and doesn't describe the return shape, but the provided guidance is enough to invoke the tool correctly in the common case.

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 0%, so the description must compensate. It explains the critical semantics: group_by enum values, required date_from/date_to, one scope filter (ad_account_id or product_id), attribution values, and view_mode daily behavior. It doesn't explain every parameter (e.g., consistency, require_complete_range, query_contract_version), but it covers the most decision-critical ones with enough detail to make correct calls.

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 aggregates insight rows for the authenticated tenant, is paginated and sortable, and is a cache-only read from tiktok_insights_daily. It also distinguishes itself from related tools like insights_pull_insights and insights_query_rows by naming them and explaining the relationship.

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 explicitly says to call insights_pull_insights first if data is stale, and notes that attribution custom 1d/7d windows are unsupported. It also provides REQUIRED parameter guidance, DATE PARAMS clarification, and a concrete EXAMPLE, which gives an agent clear when-to-use and how-to-use instructions.

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