V01D by A-to-Mind

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

Annotations cover the safety profile (readOnly, openWorld, non-destructive), and the description adds real context on top: a 12-answers-per-minute-per-caller rate limit, a 7-day cache for repeated questions (which explains the idempotentHint=false), and the fact that citations appear only when Wikipedia matches. Auth/identity requirements are not addressed, keeping it short of a 5.

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?

Front-loaded with purpose, then behavior, then limits and caching; every sentence carries information. Slightly dense in the final sentence where the rate limit and cache are combined, but nothing is wasted.

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 covers the return shape (short answer plus conditional citations) and the operational constraints. The only material gap is authentication/identity expectations and what the raw_data response contains, but annotations already signal a safe read-only call.

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 both parameters are documented in the schema, so the schema does the heavy lifting. The description restates 'plain words' and the text default but adds no syntax, length, or format detail beyond what the schema already gives. Baseline 3 is appropriate.

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 (ask) and resource (Verse's answer engine at a-to-mind.com), and describes what comes back: a short direct answer with Wikipedia citations. None of the sibling tools (drive_time, moon_phase, skill_*) are similar, so there is no ambiguity to resolve and the scope is unambiguous.

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 rather than stated: the agent can infer it is for plain-word factual questions and for questions about Verse itself, which is a genuinely useful hint. But there is no explicit when-to-use vs when-not, no guidance on alternatives, and the 'image' option is only mentioned in the schema, not routed around in the description.

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, idempotentHint and non-destructive, so safety is covered. The description adds useful context — accepted colour input syntaxes and the fact that pass/fail levels are returned — but says nothing about precision/rounding of the ratio or the exact output shape. Adequate given the annotation coverage.

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 waste: the computed result leads, and format guidance follows. No repetition 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?

No output schema, but the description discloses that the result is a ratio plus tier pass/fail, which is the essential return contract for an agent. Only the exact output structure and precision remain unstated.

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 schema names both parameters, so the baseline is 3. The description goes beyond it by enumerating accepted colour formats (#hex, rgb(), hsl(), CSS name), which meaningfully constrains how to fill both fields.

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 computation (WCAG contrast ratio of a text colour on a background) plus the derived result (which AA/AAA levels pass), which distinguishes it from unrelated siblings like readability. The verb-and-resource framing is precise.

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?

No explicit when-to-use statement or exclusion versus alternatives (e.g. the readability sibling, which an agent might confuse with accessibility checking). Usage is inferable from the purpose, but the description does not route the agent.

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 cover only the safety profile, and the description adds genuinely useful behavior the agent cannot get elsewhere: the data comes from the public OSRM router with no API key, it reflects free-flow road-network speed with no live traffic, and place names are resolved via Open-Meteo's geocoder. The free-flow/no-traffic caveat is exactly the kind of limitation that changes how a result should be presented.

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 core answer (duration + distance, routing source) is front-loaded in the first clause. The trailing sentence about Verse's rides family is tangential context that an agent doesn't need to call the tool correctly, slightly diluting an otherwise tight description.

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 carries the burden of describing returns, and it does state the two values returned (time and distance) plus the routing source. It omits units and what happens when a place name fails to geocode, which are the remaining 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 coverage is 100%, so both params are already documented, making 3 the baseline. The description goes further by naming the geocoding service used for name inputs (Open-Meteo) and confirming the 'lat,lon' string format, which adds provenance the schema does not supply.

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 resource and both outputs: how long the drive is and how far, between two places. The verb+resource+return shape is unambiguous, and no sibling tool overlaps with routing, so an agent can select it without opening the schema.

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 — the agent must infer 'use this when you need driving time/distance between two points.' There is no explicit when-to-use, when-not, or named alternative, though the note that 'Verse's rides family checks this same router' hints at related routing tooling.

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 this a read-only, idempotent, non-destructive, closed-world lookup, so the safety profile is covered. The description still adds real behavioral context: results are ordered strongest-first, each entry carries days-to-peak and peak rate, and the data comes from the IMO major-shower calendar — useful for trusting and parsing the output.

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 dense sentence that front-loads the resource and the returned content, with no filler. Every clause (ordering, peak distance, peak rate, source) 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?

With no output schema, the description compensates well by enumerating the fields a caller will receive. The one remaining gap is the default behavior when the optional date is omitted (presumably 'now'), which an agent must infer.

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?

There is one optional parameter with 100% schema description coverage, so the schema already documents the ISO date/date-time format. The description only implicitly ties this parameter to 'on a date' and does not state what happens when it is omitted, so it adds little beyond the schema.

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 resource (meteor showers) with the exact scope of the answer: showers active on a date plus the next peak on or after it. It even previews the return shape (strongest first, days to peak, peak rate) and names the data source (IMO calendar), leaving no ambiguity about what the tool answers.

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 never says when to reach for this tool or what to do instead; the only guidance ('Leave it out for now') lives in the schema, not the description. It is a single-purpose lookup, so context is implied, but no explicit when/when-not or alternative 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 cover the safety profile (readOnlyHint, idempotentHint, destructiveHint=false, openWorldHint=false), so the description is free to add other context — and it does, disclosing the exact algorithm provenance (Meeus, ch. 47-48) and the three output components. With no output schema present, naming the return values is genuinely useful behavioral disclosure, though it omits accuracy/precision caveats or the default date behavior.

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 no filler, front-loading the substantive outputs (fraction, waxing/waning, phase name) before the provenance citation. Every clause earns its place.

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 simple, idempotent, read-only calculation with full schema coverage on its one optional parameter, the description is nearly complete: it states outputs and algorithmic basis. The only material omission is the default behavior when 'date' is omitted, which the agent must infer from the schema rather than the description.

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?

There is a single parameter with 100% schema description coverage ('An ISO date or date-time (UTC). Leave it out for now.'), so the schema already carries the semantics. The description adds nothing about the date parameter (e.g., range limits, default resolution), so the baseline 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?

The description names a specific resource (the moon's phase) and enumerates exactly what is produced: illuminated fraction, waxing/waning state, and phase name. That is enough to distinguish it from the unrelated siblings (meteor_showers, ask_void, review_code) without opening the schema, though the verb is left implicit ('The moon's illuminated fraction...' rather than 'computes/returns').

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: a single-purpose astronomical calculation with no declared alternatives, prerequisites, or exclusions. The phrase 'for a moment' plus the schema note 'Leave it out for now' hints at a default-to-now behavior, but the description itself never says when to call it or when not to.

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, idempotentHint, and non-destructive behavior, so the safety profile is covered. The description adds useful context beyond annotations: it lists the specific proofreading checks performed and states 'No model,' indicating deterministic computation rather than LLM inference. It stops short of mentioning rate limits or return format details.

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 description is a single sprawling sentence with multiple clauses, which makes it dense rather than front-loaded and easy to scan. The phrase 'that Verse also runs on its own page descriptions' is irrelevant to an agent's decision and does not earn its place.

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 simple two-parameter tool with no output schema, the description explains what the result contains (readability scores, counts, proofreading fixes). Annotations cover the safety profile, and the schema covers parameter details. The only minor gap is that response_type behavior is left entirely to the 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 description coverage is 100%, so both parameters (text and response_type) are fully documented in the schema. The description does not add syntax or format details beyond what the schema already provides, making the baseline of 3 appropriate.

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 specific function: computes readability metrics (Flesch reading ease, Flesch-Kincaid grade), word and sentence counts, and proofreading checks like doubled words and misspellings. This is clearly distinguishable from siblings like skill_words or seo_audit, and an agent can tell exactly what the tool produces without opening the schema.

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?

There is no guidance on when to use this tool versus alternatives such as skill_words or seo_audit, nor any exclusions or prerequisites. The mention that 'Verse also runs on its own page descriptions' is context about the source, not usage guidance for the agent.

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 (read-only, idempotent, closed-world, non-destructive), but the description adds substantive context beyond them: no model is invoked, the basis is 'patterns', and results carry confidence 'low'. That materially changes how much an agent should trust the output, which is the kind of disclosure annotations cannot express.

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 the core purpose and immediately scoped. The a-to-mind.com sentence is marginally promotional but does convey that the engine matches the public site, so it mostly earns its place; the confidence disclosure is well placed at the end.

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 carries the return-value burden and does so partially, disclosing that the structured result reports basis 'patterns' and confidence 'low'. Combined with 100% schema coverage, an agent has enough to call the tool correctly, though the shape of the findings list itself is not described.

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%, so all three parameters are already documented, making 3 the correct baseline. The description re-frames the schema's semantics ('pasted code or a diff's added lines', 'in many languages') but adds no syntax, format or precedence rules, such as what happens if both code and diff are supplied.

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 (pattern checks for bugs, risks and style) and resource (pasted code or a diff's added lines), plus a language scope. It also carves out its boundary against review_code_closer by declaring 'No model; the closer read is not included', so an agent can tell the instant check apart from the deeper one without opening either schema.

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 establishes this is the 'instant' pattern-based check and explicitly notes the closer read is excluded, which implies when it is the wrong choice. However, it never names review_code_closer or states the condition that should route the agent to it, leaving the selection rule 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?

Goes well past the annotations (readOnlyHint/openWorldHint/non-destructive), which only cover the safety profile. It discloses the auth requirement, the accepted credential formats, the degraded-but-successful behavior without a key, and how to interpret the result via the basis ('closer_read' vs 'patterns') and confidence ('reviewed' vs 'low') fields — exactly the kind of hidden behavior an agent needs.

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?

Front-loads the purpose, then the auth requirement, then the result-interpretation rule; every sentence carries operational information. Mild redundancy in repeating 'the closer read' and a slightly promotional tone keep it from a 5.

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?

No output schema exists, and the description compensates by explaining the result's basis/confidence fields, plus the credential prerequisites and fallback path. The remaining gap is sibling routing — it never says how this relates to `review_code` — which matters given how many review-adjacent tools are listed.

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 each of code, diff, and lang is documented in-schema, including the 60,000-character cap and the diff-mode semantics. The description adds nothing about how parameters affect the two review stages or how code and diff interact, so baseline 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 and resource (reviews code) plus the two-stage mechanism: instant checks followed by an AI reviewer that validates, drops, and adds findings with line quotes. That is far more concrete than a name restatement. It stops short of distinguishing itself from the sibling `review_code`, which by naming appears to be the plain variant, so the agent must infer the difference.

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 does give concrete operational guidance: a key in the Authorization header is required for the closer read, with the exact key prefixes and a URL, and the no-key fallback is spelled out. However, there is no statement of when to choose this tool over `review_code` or when either should be preferred, which is the main selection decision an agent faces here.

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 readOnly, idempotent, non-destructive, and closed-world. The description adds useful behavior beyond them: it discloses that links are listed but not fetched (no network access) and that this is the same audit run pre-ship, giving the agent confidence about determinism.

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 checks are front-loaded in a single dense sentence that lists each audited element, and each clause earns its place. It is slightly run-on but not padded.

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 carries the return-value burden and does so by enumerating what is audited. It doesn't fully specify the result shape or how failures are reported, but it is sufficient for correct invocation.

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 meaning by explaining why path matters ('so the canonical can be checked'). It doesn't explain response_type, but the enum and description in the schema cover that adequately.

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 (SEO audit of one page's HTML) and enumerates exactly what is checked: title, meta description, canonical, JSON-LD, h1, lang, og tags, internal links, and Flesch-Kincaid grade. This clearly separates it from siblings like readability, which only covers reading ease.

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 implies usage with 'Give the page's own path so the canonical can be checked' and clarifies 'Links are listed, not fetched,' but never states when to use this versus the readability sibling or when it isn't appropriate. The context is implied rather than explicit.

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?

Goes meaningfully beyond the annotations: it discloses that a deterministic server-side skill (no model) answers, that out-of-scope asks are refused rather than guessed, that times use the server's UTC clock, and that the card is reachable via the 'open' link in the result. These are real behavioral facts an agent could not infer from readOnlyHint/openWorldHint alone.

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 description is bloated with self-promotional text ('proven skills with the most evidence', 'rated proven on a-to-mind.com from evidence...') that does not help an agent decide or invoke. The genuinely useful operational facts are buried mid-sentence rather than front-loaded.

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 two-parameter read-only tool with no output schema, it covers the essentials: nature of the answerer, refusal behavior, timezone handling, and where the full result lives. The main omission is any indication of what the text response looks like or how the 'open' link is surfaced.

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%, so the schema already explains 'ask' and the response_type enum. The description adds only example ask phrasing, which is marginal, and says nothing about the 3–600 character constraint beyond what the schema states.

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 purpose (answering constellation questions server-side) is only fully clear after reading the example asks; the opening clause 'Ask Verse's constellation skill' is framed as a marketing claim rather than a verb+resource statement. It also fails to differentiate from related siblings like moon_phase, meteor_showers, and skill_eclipse, which an agent might plausibly confuse it with.

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 through the example asks ('what's that constellation', 'where is Orion'). It states that unsupported asks are refused rather than guessed, which is useful, but never says when to pick this over the astronomy-adjacent siblings or what kinds of questions it explicitly does not take.

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?

Beyond the annotations, the description discloses genuinely useful behavior: the skill's own server-side code answers (no model), it cites sources, it refuses rather than guesses out-of-scope asks, and it reports server-clock UTC times. It also points to an "open" link carrying the card, which hints at the return shape. This is meaningful added context on top of readOnlyHint/openWorldHint.

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 definition is padded with promotional, self-referential content ("rated proven on a-to-mind.com from evidence... never from how it describes itself") that does nothing to help an agent invoke the tool. The substantive facts—server-side answering, refusals, UTC times, open link—are buried behind that clutter and the cryptic "(Sky and earth)" parenthetical.

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 two-parameter, read-only tool with full schema coverage and no output schema, the description covers the domain, refusal behavior, determinism, and where the result's card lives. The only omission is any mention of the raw_data output mode, which the schema handles adequately.

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%, so the schema already explains both 'ask' and 'response_type' in detail. The description reinforces 'ask' with example phrasing but never mentions the response_type options (text/raw_data), leaving that entirely to the schema. Baseline 3 is appropriate.

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 sentence names the skill and its domain ("country"), and the example asks ("capital of australia", "population of japan") make the resource concrete. It is clear the tool answers country-fact questions, but nothing in the text distinguishes it from the many sibling skill_* tools beyond the domain examples.

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 through the example asks; there is no explicit statement of when to prefer this over skill_distance, skill_translate, or other siblings. The line "an ask the skill does not take is refused, not guessed" signals a boundary exists but never tells the agent where that boundary is.

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 read-only/open-world/non-idempotent, and the description adds real behavior beyond them: the skill's own deterministic server code answers (no model), sources are cited from its card, unsupported asks are refused rather than fabricated, and the card lives at the "open" link in the result. The UTC clock note is irrelevant noise for a definition tool but does not undercut the disclosure.

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 opening sentence is bloated with marketing filler ("one of the proven skills with the most evidence", "rated proven on a-to-mind.com from evidence... never from how it describes itself") that gives the agent nothing actionable. The genuinely useful content — deterministic server code, refusal behavior, result link — is buried mid-paragraph.

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 simple, fully-specified 2-parameter read-only tool, the description covers the execution model, refusal semantics, and where the human-readable card is returned, and it correctly leaves return shaping to the response_type enum. No output schema exists, and the description adequately signals what comes back.

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 both parameters (including the response_type enum and the note that 'image' is not offered yet) are fully documented in the schema. The description adds no parameter-level meaning beyond the examples already present in the schema, so baseline 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?

The description names a specific verb+resource (ask Verse's "define" skill) and anchors it with example asks like "define serendipity", so an agent knows this returns word definitions. It doesn't differentiate from near siblings such as skill_words or skill_translate, and the parenthetical "(Words and the world)" adds ambiguity rather than clarity.

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 conveyed only by example phrases ("define ephemeral", "define ubiquitous") and the note that unsupported asks are refused rather than guessed. There is no explicit statement of when to prefer this over skill_words or skill_translate, so the agent must infer the boundary.

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 (readOnly, idempotent, non-destructive, closed-world), so the bar is lower. The description still adds real context: the backing source (a-to-mind.com/skills/families.json), that ratings come 'from evidence only', and that the 'image' response type is not yet offered. It omits pagination or result-size behavior.

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?

Purpose and scope are front-loaded in the opening clause, and there is no filler. The core sentence is very dense, enumerating five return fields in one breath, which makes it slightly harder to parse than it needs to be.

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 carries the return-value burden and does so well: family id, name, purpose, count, share of distribution, and member skills with ratings. Missing only edge behavior such as invalid family ids or any limits on result size.

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 a 3 baseline applies and the description only needs to add meaning. It does: 'rated from evidence only' explains what the tier scale represents, and 'image is not offered yet' pre-empts a valid enum value the agent might otherwise try.

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 and resource ('what skills Verse has and how they are spread across its families') and immediately scopes it as a pre-query discovery tool. It is clearly distinguishable from the sibling skill_* tools, which each expose a single skill, and from ask_void, which is the actual interrogation path.

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?

Gives both the condition for use ('for an agent that wants to know where to look before it asks') and the alternative path ('Ask a skill by name through ask_void or the skill's own tool'). The filter conditions ('ask for one family, or only the skills at a rating') are stated too, so the routing decision is fully specified.

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 flag read-only and open-world behavior, while the description adds meaningful context: the answer comes from the skill's own server-side code, unsupported asks are refused, times use the server's UTC clock, and the result links to a card. The promotional "proven" framing and lack of error or rate-limit detail keep it from being fully transparent.

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 opening identifies the skill, but much of the text is promotional evidence and rating language ("one of the proven skills with the most evidence... never from how it describes itself") that does not help an agent invoke the tool. Useful behavioral details are present but buried among this noise.

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 simple read-only skill query, the description covers accepted ask examples, refusal behavior, server-side execution, and where the result card lives. Output details remain thin, but the input schema is rich and no output schema exists, so it is largely sufficient for correct invocation.

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 both ask and response_type are documented in the schema, including the enum and an example ask. The description reinforces the ask format with examples but adds no extra semantics for response_type or constraints beyond the schema.

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 names the specific skill and gives concrete distance examples such as "distance from london to paris", so the agent can tell it handles distance queries. It does not explicitly distinguish itself from the sibling drive_time tool, leaving a plausible ambiguity between distance and travel-time asks.

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 examples imply the kind of ask the tool accepts, and the description notes that unsupported asks are refused rather than guessed. However, it never says when to choose this over drive_time or other location-oriented siblings, nor does it state exclusions or prerequisites.

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?

Beyond the readOnly/openWorld annotations, it discloses that execution is server-side deterministic code with cited sources, that times are computed on the server's UTC clock, and that out-of-scope asks are refused — real behavioral context an agent cannot get from the annotations. Nothing here contradicts readOnlyHint=true or destructiveHint=false.

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 operative content (what it answers, examples, server-side computation, UTC, refusals, card link) leads well, but several clauses are promotional filler about a-to-mind.com ratings and evidence methodology that don't help an agent decide or call the tool.

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 does explain the return context (sources cited, card at the 'open' link) and the refusal behavior. It omits any mention of the raw_data/image response_type options, but those are fully covered by the 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 description coverage is 100%, so the schema already documents 'ask' (3–600 chars, with an example) and the response_type enum. The description reinforces the natural-language ask format but adds nothing about response_type, so baseline 3 is appropriate.

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 names the specific resource (Verse's 'eclipse' skill, 'Sky and earth') and gives representative asks ('next eclipse', 'next lunar eclipse', 'upcoming eclipses'), so the agent can immediately tell this is the astronomy/eclipse answer skill rather than a generic one. It also clarifies the answer is produced by the skill's own server code, not a model.

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 supplies clear triggering examples and states that an ask the skill doesn't take is refused rather than guessed, which is useful behavioral guidance. It never explicitly contrasts with the other astronomy-flavoured siblings (moon_phase, meteor_showers), so routing among those is left to the topic match.

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 this a read-only, idempotent, non-destructive, non-open-world operation, so safety is covered. The description adds genuinely non-obvious behavioral context: ratings are derived from evidence only and never from a skill's self-description, and it lists what the result contains (totals, per-family counts, share proven, failing benchmark asks, skills with their own tool). No output schema exists, so this return-content disclosure carries real weight.

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 purpose is front-loaded in the first clause, and the rating-tier enumeration is dense but informative rather than filler. The middle sentence is heavy with parentheticals and slightly slows reading, keeping it out of 5 territory.

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 and no required parameters, the description does the work of explaining what comes back, including the family-level breakdown and the evidence-based rating scale. An agent has enough to call it correctly and interpret the response, though the exact shape of the JSON is not pinned down.

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%, so both parameters are already documented in the schema, including the family id source and the response_type enum values. The description's 'Name a family...' adds only a behavioral framing (detail vs. aggregate) beyond the schema, so the baseline of 3 is appropriate.

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 specific verb+resource: it reports how far each of Verse's skills is checked, grouped family by family, and enumerates the rating tiers. It is clearly distinct from the sibling skill_* tools, which operate on other domains (recipes, translation, countries). It falls short of a 5 only because the framing sentence ('How far each of Verse's skills is checked') takes a moment to parse.

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 final sentence gives a usage cue: 'Name a family to get each of its skills with its rating and why,' which implies the family parameter drills into detail while omitting it returns aggregate totals. There is no explicit when-not guidance or reference to an alternative sibling, so usage is implied rather than prescribed.

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?

Beyond the annotations, it discloses that the skill's own server-side code (not a model) produces the answer, that unsupported asks are refused rather than improvised, that time handling uses the server clock in UTC, and that the result carries an 'open' link to the card. These are real behavioral facts an agent could not get from readOnlyHint/destructiveHint alone.

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?

Roughly a third of the text is marketing filler ('one of the proven skills with the most evidence', 'rated proven on a-to-mind.com from evidence... never from how it describes itself') that does no work for tool selection. The genuinely useful facts (examples, refusal behavior, UTC clock, open link) are buried mid-paragraph rather than front-loaded.

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 steps in to describe the return value in part (a result containing an 'open' link to the card, sources cited). Combined with full schema coverage and read-only annotations, an agent has enough to invoke it correctly; only the raw_data/image response forms go unexplained.

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%, so both 'ask' and 'response_type' are already fully documented in the schema, including the enum values. The description reinforces the 'ask' format with examples but adds nothing about response_type or any syntax beyond what the schema provides; baseline 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 (ask) and resource (Verse's 'joke' skill) and gives concrete example asks like 'tell me a joke' and 'make me laugh'. An agent can tell it apart from skill_quote/skill_words by subject matter, though the differentiation is implicit rather than stated.

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 example asks effectively define the trigger conditions, and the note that 'an ask the skill does not take is refused, not guessed' sets expectations for off-topic input. However, it never explicitly says when to prefer this over siblings such as skill_quote or skill_words, 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 declare readOnlyHint=true and destructiveHint=false, but the description adds real context: the answer is produced by the skill's own server-side code with no model, refused if out of scope, and timestamps use the server clock (UTC). This is meaningful disclosure beyond the annotations, though wrapped in promotional claims about a-to-mind.com ratings.

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 opening is dominated by self-promotional claims about evidence and ratings ('rated proven on a-to-mind.com... never from how it describes itself') before the actual function is clear. Several sentences do not help an agent decide or call the tool, and the useful details are buried mid-paragraph.

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 should explain the return value; it only says 'the card itself is at the "open" link in the result', which is thin for a tool offering text/raw_data/image response types. It does cover the read-only nature and refusal behavior, so it is adequate but incomplete on result shape.

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%, so both 'ask' and 'response_type' are already documented in the schema, including the enum options. The description only reinforces the ask examples ('inspirational quote') and adds nothing about length limits or the raw_data format, so baseline 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?

The description names the specific skill ('quote', 'Words and the world') and gives concrete example asks, so an agent can tell it returns quotes and distinguish it from siblings like skill_joke or skill_define. It is muddied by marketing framing ('proven skills with the most evidence') rather than a clean verb+resource statement.

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?

Examples ('inspirational quote', 'random quote', 'tell me a quote') imply the shape of a valid ask, and it notes that unsupported asks are refused rather than guessed. However, there is no explicit when-to-use/when-not guidance and no routing to alternatives among the many skill_* siblings.

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?

Beyond the readOnly/openWorld annotations, the description discloses that the skill's own server-side code answers (no model involved), that out-of-scope asks are refused rather than guessed, that time handling uses the server's UTC clock, and that a card link appears in the result. These are substantive behavioral facts an agent could not infer from annotations alone.

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 opening is dominated by self-promotional framing ('one of the proven skills with the most evidence', 'rated proven on a-to-mind.com from evidence') that consumes roughly half the text before any actionable information. The actual purpose surfaces late, in the example asks, and the a-to-mind.com rating justification earns no place in a tool definition.

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 two-parameter, no-output-schema tool the description covers enough to invoke it correctly (what to ask, refusal behavior, response_type covered by schema), but it spends its budget on provenance marketing rather than on what a returned answer looks like beyond a card link. Adequate but with clear slack.

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%: the 'ask' parameter has a length bound and an example, and 'response_type' fully documents its enum values including that 'image' is not yet offered. The description only repeats the same example ask, adding no new constraint or format guidance beyond the schema, so baseline 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?

The verb+resource combination ('Ask Verse's swaps skill') plus concrete example asks ('what can i swap for buttermilk', 'substitute for eggs in baking') make the substitution-lookup purpose unambiguous. However, it never distinguishes itself from sibling skills like skill_words or skill_define, which an agent could plausibly confuse for word/ingredient questions.

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 through the sample asks; there is no explicit statement of when to pick this over skill_words or other siblings. The line 'an ask the skill does not take is refused, not guessed' hints at boundaries but never says what falls outside scope.

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?

Beyond the read-only, open-world annotations, the description discloses that the skill's own server-side code answers without a model, cites sources from its card, refuses asks it does not take, and uses UTC for times. These are useful behavioral details, though the description repeats read-only and spends substantial space on promotional evidence rather than operational context.

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 purpose and examples are front-loaded, but the description is bloated with marketing claims about being a proven skill, benchmark evidence, and a-to-mind.com provenance. Several sentences do not help an agent decide when or how to invoke the tool, so the definition is not appropriately concise.

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 simple two-parameter read-only translation skill with full schema coverage, the description is complete enough to support invocation: it explains what kinds of asks are accepted, that unsupported asks are refused, and that results point to a card link. It could be improved by explicitly describing the return shape, but the combination of schema and annotations covers most needs.

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%, so both the required ask parameter and the response_type enum are already documented in the schema. The description reinforces the ask format with examples but adds no additional meaning for response_type or other parameter behavior, matching the baseline of 3.

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 makes the purpose clear through the named skill and concrete examples such as "translate hello to spanish" and "translate thank you to japanese". It does not explicitly distinguish this tool from sibling skills like skill_words or skill_define, so it falls short of a 5.

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 examples imply the kinds of asks the tool handles, and the description notes that unsupported asks are refused. However, it gives no explicit when-to-use versus alternatives guidance and does not name sibling tools that might be confused with this one.

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 read-only, open-world, non-idempotent, non-destructive behavior. The description adds meaningful context beyond that: answers come from server-side code rather than a model, refused asks are not guessed, sources are cited, UTC clock is used, and a card link is returned. That is useful behavioral disclosure, though the marketing framing dilutes it.

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 text is padded with self-promotional claims ('one of the proven skills with the most evidence', 'rated proven on a-to-mind.com') that do not help an agent select or invoke the tool. The useful facts are buried among parentheticals and marketing copy.

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 should more clearly explain what the text and raw_data responses contain beyond a link to a card. It covers refusal behavior and deterministic answer sourcing, but leaves the return shape underspecified.

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%, so both parameters are already documented. The description offers example asks but no additional syntax, constraints, or raw_data semantics beyond what the schema provides. Baseline 3 is appropriate.

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 gives concrete example asks (synonyms, spelling, rhymes) that make the tool's function understandable, though the phrase 'Words and the world' is vague. It does not explicitly distinguish itself from potentially overlapping siblings like skill_define or skill_translate, so it stops short of a 5.

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?

There is no guidance on when to use this tool versus alternatives, nor any explicit exclusions beyond 'an ask the skill does not take is refused.' The examples imply usage but do not constitute routing guidance.

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