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

Annotations already declare readOnlyHint, destructiveHint=false, openWorldHint and non-idempotency, so the safety profile is covered. The description adds genuinely useful behavior: the report is model-generated rather than inferred from raw scores, report richness scales with the requested features, and omitting `advanced_report` suppresses the report entirely. It does not mention the async/polling nature of the run, the zip-vs-json result shape, or latency, which are left to the schema.

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 structure is good — bold lead-ins, a feature table, a parameter table — and the 'what it is' statement is front-loaded. However, sentences like 'The model does not guess at your skin' and 'both tones stay professional' are promotional filler, and the parameter table duplicates defaults and enums already present in the schema, so the definition is longer than its informational payload.

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?

For a nested, option-heavy tool with an output schema and full annotation coverage, the description covers the parts that matter for correct invocation: required `dst_actions`, the HD/SD mixing rule, the report opt-in, and the report's language/tone/climate options. The `format` (zip vs json), `polling`, `src_file_url` vs `src_file_id`, and `miniserver_args` are left entirely to the schema, which is acceptable given 100% schema description coverage and an existing output schema.

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%, so the baseline is 3, but the feature-to-report-dimension table adds meaning the schema does not carry: it explains which `dst_actions` values produce skin type, T-zone/U-zone, wrinkle regions, pore regions and texture, and which facial regions each returns. It also documents `pf_camera_kit` (undescribed in the schema) as driving face outline and feature localization. The `language`/`tone`/`country`/`city` table largely restates the schema, which keeps it below a 5.

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 paragraph states a specific resource and output: a natural-language skin report generated by an LLM from measured skin metrics plus a skincare reference database, written to `llm_results.json`. That is concrete enough to distinguish it from a plain metric-returning analysis, but the description never names or contrasts any sibling (e.g. the Standard analysis or the Product-Recommendation variant), so differentiation is left implicit.

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 strong in-tool guidance: `dst_actions` is required, HD and SD features cannot be mixed, and the table maps each requested feature to the report dimensions it unlocks. It also tells the agent how to opt in or out of the report (`"advanced_report": {}` vs. omitting it). What is missing is the selection question across siblings — nothing says when to choose this report tool over the Standard analysis or the Product-Recommendation versions.

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