get_face_typeFetches the definition, front-photo measurement rule, and page URL for a specific face type by category and slug, so you can identify or cite shape criteria.
Definition and how the type is measured from a front photo (the exact threshold rule), plus its page URL. Example: category 'face', slug 'oval'.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | e.g. oval, heart, hooded, upturned | |
| category | Yes |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=false, so the safe, closed-world lookup profile is covered. The description adds content-level transparency (it returns the definition, the exact threshold rule, and a page URL), which is meaningful without an output schema, but says nothing about invalid category/slug handling or lookup failures.
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?
Two sentences, zero filler: the first front-loads the returned payload (definition, threshold rule, URL) and the second supplies a concrete example. Nothing is wasted or buried.
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?
With no output schema, the description correctly carries the return-value burden by naming the three things returned, which is complete enough for a simple two-parameter lookup. The only gap is error/edge-case behavior for unrecognized categories or slugs.
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 only 50% — the slug property carries examples while the category enum has no prose. The description compensates by demonstrating a valid pairing (category 'face', slug 'oval'), clarifying that both are identifiers rather than free text, though it adds no detail on slug formatting or invalid-value behavior.
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?
States a specific verb (retrieve/describe) and resource (one face/eye/lip/nose type), and enumerates the exact payload: definition, measurement rule from a front photo, and page URL. It is clearly distinguishable from the plural sibling list_face_types by virtue of fetching a single item, though it never names that sibling explicitly.
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 implies usage — look up one type by category+slug to get its definition and threshold rule — via the worked example, but it offers no explicit when-to-use guidance or exclusions relative to list_face_types, classify_face_shape, or get_reference_statistics. Usage must be inferred from the tool name and payload.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.