FaceRuler

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so safety is covered. The description adds genuinely useful behavior beyond that: it is a deterministic ratio-based computation (not image analysis) and returns a match percentage for every shape rather than a single label, which matters since no output schema exists.

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?

A single front-loaded sentence with the action first, followed by the input basis and the return format. No filler, no restatement of the name, every clause carries information.

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 small deterministic classifier with fully documented parameters and no output schema, the description supplies the missing return shape (per-shape match %) so the agent knows what to expect. It stops short of noting behavior for out-of-range ratios or that results are heuristic, which are minor gaps.

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 each parameter carries its own formula and typical range in the schema. The description only summarizes them as 'three measured outline ratios,' adding no syntax, defaults, or units beyond what the schema already provides, so the baseline of 3 applies.

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 (classify) and resource (face) and enumerates the full output space of 7 shapes plus the three input ratios, so an agent knows exactly what it produces. The only gap is that it never names or contrasts with siblings such as get_face_measurement_tool, which supplies the ratios this tool consumes.

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 implied: the tool needs three measured ratios, which tells the agent it applies once measurements exist. However, it never states the relationship to get_face_measurement_tool (the obvious upstream step) or any when-not-to-use condition, leaving routing to inference.

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

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

Annotations already cover the safety profile (readOnlyHint=true, openWorldHint=false), and the description adds real context beyond that: the link is free, runs on-device, and needs only one photo — which implies no data upload. It stops short of describing what the link returns or any constraints on the resulting measurement.

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, front-loaded with the core action and scoping, with no filler. The parenthetical value list slightly duplicates the enum, costing a little efficiency but it aids scanning.

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 one-parameter, read-only tool with no output schema, the description covers what is returned (a link) and the trigger. Missing only the fallback routing to sibling lookup/classification tools, which is a minor gap given the simple surface.

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% and the single enum parameter is fully documented, so the baseline is 3. The parenthetical lists the exact enum values (PSL, face shape, canthal tilt, facial ratios), which merely restates the schema rather than adding format or selection meaning.

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 description uses a specific verb+object ('Return the link to FaceRuler's free on-device tool') and scopes it to measuring a real face from one photo. It implicitly distinguishes itself from the lookup/classify siblings by being a tool-link provider, but never names those siblings, so the boundary with psl_score_lookup and classify_face_shape is left to inference.

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 a clear trigger: 'Use when the user wants their own face measured.' That is a usable condition, but there is no guidance on when NOT to use it (e.g., when the user wants an existing score looked up or a shape classified instead), so the agent must infer the alternative.

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

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

The annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds one useful behavioral fact — that it links to a full explanation rather than returning the definition inline — but says nothing about unknown/near-miss terms, case sensitivity, or ambiguity handling.

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?

A single compact sentence with the action front-loaded, the domain qualifier mid-sentence, and the return behavior at the end. Nothing is wasted and nothing is 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?

For a one-parameter, read-only lookup with no output schema, the description covers purpose and the shape of the result (a link to a full explanation). It only falls short on failure/edge-case behavior, which is a minor omission at this complexity.

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% with a single 'term' string, so the schema already carries the semantics. The description's example list (LTN, HTN, Chadlite, canthal tilt, hunter eyes, mog) does hint at the accepted vocabulary, which is mild added value, but no matching rules or format guidance is given.

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 description states a concrete verb+resource ("Define a looksmaxxing / PSL term") and enumerates example terms, so the agent knows exactly what it returns. It is not explicitly differentiated from the closest sibling psl_score_lookup, but the resource ('term definition') is distinct enough to be recognizable.

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 by the word 'look up' — the description never states when to prefer this over psl_score_lookup or the face measurement tools. No prerequisites, no when-not conditions, no disambiguation from siblings is offered.

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

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered and the bar is lower. The description adds genuine value by naming the return fields (tier name, approximate population percentile, casual 1–10 equivalent), which matters because there is no output schema. It doesn't state determinism or rounding/precision behavior, but nothing is contradicted.

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?

A single sentence with zero filler: the operation, the input domain, and the outputs are all front-loaded. Every clause earns its place with no restatement of the tool name or title.

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 one-parameter, no-output-schema conversion tool, the description supplies the essential missing piece by describing what is returned. What is absent is minor edge-case context (rounding of fractional PSL values, invalid-input behavior), which the schema's min/max partly covers.

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 the range 0–8 is already enforced by minimum/maximum in the schema, so the baseline is 3. The description adds a little meaning by identifying the scale as the 'looksmaxxing face rating scale', but supplies no extra syntax, unit, or precision guidance beyond that.

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 description states a precise verb (convert) and resource (a PSL score on the 0–8 looksmaxxing face rating scale) and enumerates the three transformed outputs. An agent can immediately distinguish this from siblings like classify_face_shape, get_face_measurement_tool, and lookup_looksmaxxing_term, which deal with shape, measurement, and terminology respectively rather than numeric conversion.

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 implied by the purpose: call it when you hold a PSL value and need its tier/percentile/1–10 mapping. However, there is no explicit when-to-use framing, no mention of the potentially overlapping lookup_looksmaxxing_term sibling, and no note on out-of-range or non-integer inputs. Adequate but with clear routing gaps.

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