production.facial_analyze_shape_keys

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

Annotations only give the generic safety profile (readOnlyHint=false, destructiveHint=false, idempotent=true), so the description adds real value: plan-first with apply defaulting to false, 'facial' being the immutable plan.json reference, candidates written as new Actions via NLA tracks, the body Action never modified, and the TIMING_INPUT_REQUIRED prerequisite for speech. That is concrete mutation/state behavior beyond the annotations.

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 text is dense and front-loads an internal codename ('M7 facial unit') that does not help selection, but the remaining clauses each carry a real rule. It reads as a compressed run-on of implementation notes rather than a structured description.

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, and the safety profile is annotation-covered. However, for a six-parameter, zero-coverage tool, the description leaves too much undefined — the core purpose is only implied and four parameters are unexplained.

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% and all six parameters are bare types. The description only clarifies two of them (apply defaults to false/plan-first; facial is the immutable plan.json reference) and says nothing about brief, intent, options, or object_name, leaving most inputs 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 opening 'facial_analyze_shape_keys: M7 facial unit' merely restates the tool name plus an internal code, and the rest is jargon ('Evidence-based semantic mapping (never names only)'). It never plainly states what the tool analyzes (shape-key semantics?) or how it differs from nearby analysis tools like facial_analyze_curves, facial_analyze_bones, or facial_analyze_hybrid.

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?

There are implicit workflow rules ('plan first, apply=false by default', 'Speech needs explicit timed phonemes'), but no explicit statement of when to choose this tool over the many facial_analyze_* siblings, and no when-not-to-use guidance.

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