production.facial_analyze_lips

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

Beyond the annotations it discloses concrete behavioral facts: candidates are new Actions on NLA tracks, the body Action is never modified, and the 'facial' reference is immutable. It also flags a constraint/error condition (speech requires explicit timed phonemes/visemes, else TIMING_INPUT_REQUIRED), which is valuable non-obvious context. Annotations already cover safety hints, so this is a strong addition.

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 name and plan/apply behavior are front-loaded and each clause carries some information, but 'M7 facial unit' and 'Evidence-based semantic mapping (never names only)' are cryptic filler that consumes space without informing the agent.

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?

With an output schema and annotations present, part of the burden is lifted, and the description covers the plan/apply workflow, immutability, candidate creation, and the speech timing constraint. However, it omits parameter meanings and any differentiation from sibling facial_analyze_* tools, leaving an agent unable to confidently select 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?

Schema description coverage is 0% across six optional parameters, so the description must carry the load. It only clarifies 'apply' (default false) and 'facial' (immutable plan.json reference); 'brief', 'intent', 'options', and 'object_name' are entirely unexplained.

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 name implies lips analysis, but the description only offers jargon like 'M7 facial unit' and 'evidence-based semantic mapping' without stating what the tool actually analyzes or produces. It never distinguishes itself from the many siblings (facial_analyze_jaw, eyelidseyes, hybrid, etc.), so an agent cannot tell why it would pick this over them.

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?

'plan first, apply=false by default' gives a workflow habit, and 'Technical QA and artistic review stay separate' hints at staging, but there is no explicit when-to-use or when-not, and no routing to a sibling alternative. The agent is left to infer when lips analysis applies versus other facial units.

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