Recommend hairstyles for a face shape, with links to virtually try each one on your own photo.
Scored across 1 tool
With only one tool, there is no possibility of misselection or overlapping purpose. The single tool's purpose (recommend hairstyles by face shape, gender, and length) is stated unambiguously.
The lone tool uses a clear snake_case verb_noun pattern (recommend_hairstyles) that would fit a larger set well. A single tool makes a consistent pattern impossible to fully verify, so it falls just short of a 5.
One tool is thin for a domain that involves catalogs, face shapes, and genders; it feels more like a single endpoint than a coherent toolkit. It is not unreasonable for a narrow recommendation service, but it sits at the borderline.
The tool covers the core recommendation flow and returns actionable links, but there is no way to browse or list the hairstyle catalog, fetch details for a specific hairstyle, or filter by other attributes. Notable gaps exist around discovery beyond a single recommendation call.
Recommend up to 4 curated hairstyles that suit a face shape, from the Style Look Lab hairstyle library. Each result has a name, a short description, a link to try the hairstyle on your own photo, and (when available) a link to the full hairstyle guide. face_shape: oval, round, square, heart, diamond, oblong or triangle. gender: female or male (selects the hairstyle catalog). length: optional filter, short, medium or long.
| Name | Required | Description | Default |
|---|---|---|---|
| gender | Yes | Hairstyle catalog to pick from | |
| length | No | Optional hair length filter | |
| face_shape | Yes | Face shape |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and does well: it discloses the 4-result ceiling, the catalog source, that gender selects the catalog, and the structure of each returned item including optional fields. It stops short of stating that the call is a read-only lookup with no side effects or what happens for combinations with no matches.
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?
Three sentences, front-loaded with purpose followed by result shape and parameter values. It is efficient, though the re-listing of the enum values already present in the schema is mildly redundant.
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?
There is no output schema, and the description compensates by describing the returned fields and their optionality, which is exactly what an agent needs to consume the result. No annotations exist, so the missing explicit read-only/no-side-effect statement is the only remaining gap.
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 100% and all three parameters are enums already documented in the schema, so the schema does the heavy lifting. The description enumerates the allowed values and clarifies gender's role in catalog selection, but adds no format or behavior detail beyond the schema (e.g. what the default or no-filter length behavior is).
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?
Names a specific verb (recommend) plus the resource (curated hairstyles) and the selection criterion (face shape), and names the source library (Style Look Lab). It further states the result cap of 4 and what each result contains, so the agent knows exactly what it gets.
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?
The description makes the invocation context clear: supply a face shape and gender, and optionally a length filter. There are no sibling tools and no stated exclusions, so there is nothing to disambiguate against, but it also never says when this tool should not be used.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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