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

Annotations cover safety (readOnly, idempotent, non-destructive) so the bar is lower, yet the description adds substantial behavior: the exact return shape including nested action_data, family, page scope, and the fact that result is null until the measurement window closes. This is exactly the context an agent needs to interpret and continue after the call.

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?

Front-loaded with purpose and canonical-surface claim, then structured into recipes and an Args list. It is long, but the length is earned by the 14-param surface and the enumeration of return fields; the return-field sentence is dense but useful.

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?

No output schema exists, so the description must describe returns, and it does thoroughly (field list, null-result timing, deep_link, evidence shape). Combined with the recipes and full parameter coverage, an agent has everything needed to call and interpret 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?

With only 7% schema description coverage, the description carries nearly the full burden and mostly succeeds: it explains status semantics (open = pending+in_progress+snoozed), all category/source/action_type values, lens, quick_win, search scope, and sort options with defaults. The only gap is user_intent, which appears in the schema but is not addressed in the description.

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 a specific verb+resource ('List recommended actions for a brand') and positions it as the canonical 'what should the brand do' surface that unifies all Trakkr signals. This clearly distinguishes it from siblings like get_action_stats, get_results, and manage_action.

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 says when to use it (canonical queue surface) and provides four concrete recipes showing filter combinations for distinct workflows. It also names get_results as the alternative for measured results, and all signals that feed the queue.

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