Face measurement statistics

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

Annotations cover the safety profile (readOnlyHint=true, openWorldHint=false), so the description's job is to add context — and it does: dataset provenance (102 faces, 49 women/53 men, one city), the measurement pipeline (478-point MediaPipe), and an explicit limitation that these describe a specific research set, not a population. It also names the returned percentile set, which compensates for the absent output 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?

Three dense sentences that are front-loaded with the substantive content (what is measured) before the scope caveat. The measure list is long but each item earns its place; nothing is padding.

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 read-only reference-statistics tool with full schema coverage and no output schema, the description supplies the data provenance, the returned statistics, and the generalizability caveat an agent needs. Only a note on how results are structured per group/measure combination is arguably missing, which is minor.

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 description adds real value by expanding the cryptic enum keys into human terms (fwhr → fWHR, pd → pupillary distance, tilt → canthal tilt) and restating the group breakdown. This goes beyond what the raw enum lists convey.

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+resource: percentile statistics of facial measurements from a named dataset, with the exact measures enumerated. It is clearly distinguishable from classify_face_shape or get_face_type by being a static reference table, though it never explicitly names a sibling or its contrast with them.

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?

Usage is only implied: an agent can infer this is the lookup for population-reference percentiles to compare a face against. There is no explicit when-to-use, no statement of when NOT to use it versus the classification siblings, though the closing caveat about generalizability does guide interpretation.

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