production.facial_mocap_cleanup

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

Beyond the annotations (readOnlyHint=false, destructiveHint=false, idempotentHint=false), the description discloses key behavior: a two-phase plan/apply workflow, that the body Action is never modified, and that candidates land as new Actions on NLA tracks. It also documents the failure mode for missing phoneme timing. It is short on permission/reset semantics, but it adds substantive context the annotations do not.

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?

Dense and telegraphic, with semicolon-chained clauses that pack information but hurt readability. The opening 'facial_mocap_cleanup: M7 facial unit' restates the name rather than front-loading the actual behavior. Every sentence does carry content, but the structure works against scanning.

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?

Having an output schema removes the need to describe return values, and the plan/apply plus non-destructive-candidate behavior is covered. But for a 7-parameter mutation tool with zero schema descriptions, leaving five parameters unexplained is a real gap an agent must guess around.

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 7 parameters, so the description carries full burden. It usefully explains 'apply' (default false) and clarifies that 'facial' is the immutable plan.json reference, but brief, intent, options, object_name, and operation_id are left entirely undocumented in both schema and prose.

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 verb 'cleanup' is vague on its own; what the tool actually does must be inferred from the clauses about evidence-based semantic mapping, creating candidate Actions through NLA tracks, and never modifying the body Action. It never names the sibling tools it competes with (facial_mocap_analyze, facial_mocap_map, facial_mocap_retarget), and 'M7 facial unit' is internal shorthand an outside agent cannot parse.

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?

Implied workflow guidance is present ('plan first, apply=false by default'), which tells the agent to run in dry-run mode before committing, and there is an explicit precondition for speech input ('TIMING_INPUT_REQUIRED otherwise'). However, it never states when to choose this tool instead of facial_mocap_analyze/map/retarget, nor when not to use it.

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