production.facial_generate_coarticulation

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

The description discloses useful behavior beyond the annotations: candidates are new Actions evaluated through NLA tracks, the body Action is never modified, facial is an immutable plan.json reference, and apply defaults to false. But it also describes a write path ('apply', 'Candidates are new Actions') while annotations declare readOnlyHint=true, which misrepresents what happens if an agent sets apply=true.

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?

It is compact and clause-based with no filler sentences, but it is front-loaded with the opaque fragment 'M7 facial unit' rather than a plain statement of what the tool does, which delays comprehension.

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?

An output schema exists so return values need not be explained, but the description assumes familiarity with plan.json, NLA tracks, and 'M7' while leaving three of six parameters undocumented. For a tool with a plan/apply split and a stated annotation tension, an agent is left guessing on key details.

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 parameters, so the description must carry the load. It explains apply (default false) and facial (immutable plan.json reference) and hints at intent via 'semantic mapping', but brief, options, and object_name are never mentioned and no parameter takes a documented format.

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 generating coarticulation, and the description adds that it produces a plan (apply=false by default) and candidates as new Actions, without touching the body Action. But the actual purpose is obscured by opaque jargon ('M7 facial unit', 'Evidence-based semantic mapping') and it never states concretely what coarticulation output is, nor how it differs from siblings like facial_generate_lipsync, facial_map_visemes, or facial_generate.

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?

It gives a real workflow rule ('plan first, apply=false by default') and a precondition ('Speech needs explicit timed phonemes/visemes, TIMING_INPUT_REQUIRED otherwise'), which are genuinely useful. However, it never says when to choose this over the many adjacent facial generation/mapping tools in the sibling list, leaving routing to inference.

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