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

Annotations only declare it is a non-read-only, open-world, non-idempotent call; the description adds real substance by disclosing that it opens a dialog in every Frapea tab, returns an actionId, and that the user must be told to confirm in the assistant reply. It stops short of warning about calling it repeatedly (which the non-idempotent hint implies) or what happens if the user declines, so not a 5.

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?

Three tight sentences: what it does, the side effect plus required return handling, then the trigger condition. Front-loaded and every clause carries operational information.

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, yet the description still names the return value (actionId) and describes the multi-step confirmation loop that follows, plus the entry condition. For a dialog-triggering tool this is complete enough to call correctly on the first attempt.

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 coverage is 100% and both parameters (token, projectId) are fully described in the schema, so the description adds no parameter-level meaning beyond what is already structured. Baseline 3 applies.

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 and resource (asks the user to enable on-device AI analyzers) and names the concrete features affected (picture search, speech). It also situates itself in the flow relative to get_media, await_user_action, and get_analysis_status, so an agent can distinguish it from those siblings without opening schemas.

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?

Gives an explicit trigger condition ('Call this when get_media reports needs-models') and an explicit downstream procedure: tell the user in the reply, then call await_user_action and poll get_analysis_status. Nothing about when to invoke it is left to inference.

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