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

No annotations provided, so the description carries the burden. It discloses the return cardinality (13 categories) and that counts are 'live', which is useful behavioral context. However, it says nothing about caching, rate limits, or how 'free counts' are scoped (per category vs. total).

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 crisp sentences with zero waste. The result shape is front-loaded and the follow-up action is stated immediately.

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?

Adequate for a zero-param discovery tool, but with no output schema and no annotations, the description should say a bit more about what each category entry contains or what 'free counts' means. Minor gap, not a blocker.

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?

Zero parameters, so baseline is 4. The description correctly implies a no-arg invocation, consistent with the empty 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 (the 13 component categories) and the data returned (live free counts). Differentiates reasonably from siblings like aura_search_components by framing it as the entry point for category browsing, though it doesn't name a 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?

Gives a clear workflow: 'Pick one, then search within it,' which implies this is a top-level browse tool preceding aura_search_components. No explicit when-not-to-use, but the sequencing guidance is actionable.

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?

No annotations exist, so the description carries the full behavioral burden. It usefully discloses what the response contains (source, preview, URL), implying a read-only fetch, but says nothing about auth requirements, rate limits, or error behavior when an id/slug is absent.

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 tight sentences with the payload front-loaded and the input format tacked on second. No filler, though the fragment style for the identifier hint is slightly clipped.

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 takes on the job of describing the return payload and does so at a high level. However, it leaves the two input parameters under-specified (mutual exclusivity, numeric-only id) and omits any failure mode for invalid identifiers.

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 0% and the 'id' property has no type declared, so the schema gives almost nothing. The phrase 'Numeric id or slug' partially compensates by telling the agent id is numeric and slug is a string alias, but it never states these are mutually exclusive alternatives or whether one is preferred.

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 (fetch full component detail) and enumerates the payload: HTML/Tailwind source, preview image, page URL. It differentiates itself from siblings like aura_search_components by promising 'full' detail, though it never names the alternative 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 clause 'Numeric id or slug' hints that this is a lookup-by-identifier tool, but there is no statement of when to reach for it versus aura_search_components (discovery) or aura_install_component (install). No prerequisites or exclusions are given.

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?

With no annotations at all, the description carries the full burden and does disclose the concrete return payload (DESIGN.md content, preview_html, tokens, URL), which is real behavioral value since no output schema exists. It says nothing, however, about auth requirements, what happens if neither id nor slug resolves, or error behavior on an unknown system.

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 compact sentences with no filler, and the content inventory is front-loaded ahead of the key hint. The fragmentary 'System id or slug' sentence is clipped almost to the point of ambiguity, which slightly undercuts an otherwise efficient structure.

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 with no annotations and no output schema, the description partially covers the gap by enumerating returned artifacts. It remains incomplete on parameter disambiguation and failure modes, which an agent would need before calling with only one of two optional keys.

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 0% for both parameters, so the description must compensate. 'System id or slug' does clarify that the two parameters are alternate lookup keys rather than a required pair, which is genuine added meaning. It still does not say whether id takes precedence over slug or what happens when both are supplied (required count is 0, so all are optional).

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 the resource (a design system, named via id or slug) and enumerates the payload returned: DESIGN.md content, preview_html, tokens, and page URL. That is enough for an agent to know this is the full-detail retrieval call. It does not, however, contrast itself with aura_search_design_systems or aura_use_design_system, so the distinction 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?

There is no statement of when to reach for this tool versus aura_search_design_systems (discovery) or aura_use_design_system (application). The only usage cue is the terse 'System id or slug' fragment, which hints at the lookup key but not the scenario. No prerequisites, no exclusions, no alternatives are named.

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?

No annotations are provided, so the description carries the full disclosure burden. It notes the returned fields (source_url, page URL) and hints at "free" content, but says nothing about auth requirements, error behavior, or whether the skill must be installed first.

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?

Extremely short but under-specified rather than concise: two sentence fragments that read like field notes. The most useful information (returned fields) is present but no sentence fully earns its place by being self-contained.

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 one-parameter getter with no output schema and no annotations, the description does at least list return fields, but it leaves the identifier semantics and the relationship to search/install siblings unexplained, so an agent cannot confidently place it in a workflow.

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?

One parameter at 0% schema description coverage. The trailing fragment "Skill id." merely restates the schema property name 'id' and adds no format, source, or identifier-scheme detail to help the agent supply a valid value.

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 resource ("Full free agent-skill content (SKILL.md body)") and implies a fetch by id, which an agent can distinguish from aura_search_skills. However, it uses no explicit verb and reads as a fragment list rather than a statement of what the tool does.

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 when-to-use guidance and no mention of the obvious alternative, aura_search_skills, or aura_install_skill. The agent must infer that this is the retrieval step after a search.

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?

With no annotations, the description carries the burden and does disclose the return shape (dependency list, setup steps, file map, fonts), which matters since there is no output schema. However it never resolves the central ambiguity of a tool named 'install': whether it writes anything, requires auth, or is purely read-only text retrieval.

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 tight sentences with the payload front-loaded and no filler. The second sentence's competitive framing ('goes beyond the official Aura MCP') is marginally salesy but does convey a capability differentiator.

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 tool with no annotations and no output schema, the description usefully enumerates the returned artifacts, partially offsetting the missing output schema. It still leaves identifier semantics and any side effects or prerequisites unaddressed.

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 0% and neither parameter is typed or documented in the schema. The description only implies that the id/slug refer to a 'free component' and adds no guidance on which identifier to supply or whether both are needed.

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 resource (a component) and enumerates the concrete payload (dependency list, setup steps, file map, fonts), so an agent knows what comes back. It does not differentiate from the closest sibling aura_get_component, and the name 'install' collides with the description's implication that this only returns paste-ready text.

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 only usage signal is the qualifier 'for a free component', which hints at a scope limit but never states it as a rule or names an alternative. Nothing tells the agent when to call this instead of aura_get_component or aura_install_skill.

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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It hints that the output is a file-placement plan (where to put SKILL.md per client plus upstream source), but says nothing about side effects, whether files are actually written, auth requirements, or reversibility.

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?

It is a single front-loaded sentence with no filler, which is structurally efficient. However, the awkward 'Save-and-load plan' framing trades clarity for brevity rather than genuinely earning its compactness.

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 annotations, no output schema, and an undocumented required parameter, the description is too thin to let an agent call the tool correctly. It should at minimum clarify the action performed, the meaning of 'id', and what the returned plan contains.

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?

The single required parameter 'id' has 0% schema description coverage and the description adds nothing about it. Worse, the description implies per-client variation, yet no client parameter exists, so the meaning of 'id' (skill id? client id?) is left ambiguous.

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 name says 'install_skill' but the description calls it a 'Save-and-load plan', leaving it unclear whether the tool performs the install or merely returns placement instructions. It does add resource detail (SKILL.md, per-client placement, upstream source) beyond the name, but the core action is vague and it never distinguishes itself from the sibling aura_install_component.

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 when-to-use guidance, no prerequisite, and no mention of alternatives such as aura_get_skill or aura_install_component. The agent is left to infer the entire usage context.

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?

No annotations are provided, so the description carries the full burden, but it does disclose useful behavioral detail: results are curated ('top'), filtered to 'free' items, and include page URLs plus rationale. It does not say whether the call is read-only, how many items come back, or how 'top' is ranked, leaving meaningful gaps for an unannotated tool.

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?

One tightly packed sentence-fragment, front-loaded with the tool's role and then the returned item types. Nothing is wasted, though the telegraphic style gives no room for the missing usage and parameter guidance.

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 annotations, the description must carry the return contract, and it partially does by listing the artifact categories and noting URLs and rationale. It omits result count, ranking basis, and any permission or read-only context, leaving the definition only minimally adequate for a one-parameter tool.

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?

The single 'goal' parameter has 0% schema description coverage, so the description is the only source of meaning — and it only echoes the schema by saying 'for a goal.' It does not clarify expected format (free text vs. keywords), scope, or how specificity affects the result, so it fails to compensate for the coverage gap.

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 resource it produces (a curated starter kit of components, skills, design systems, and assets) scoped by an input goal, which is a distinct purpose from the sibling search_* and get_* tools. It stops short of naming those siblings, but an agent can tell this is a recommendation/aggregation tool rather than a lookup.

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: 'for a goal' suggests it is invoked when the agent has a goal and wants a bundled starting set rather than individual lookups. It never states when to prefer it over aura_search_all or the per-type search tools, nor any prerequisites, so the routing is left 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?

No annotations are provided, so the description carries the full behavioral burden. It discloses parallel aggregation and a 'Free only' filter, but does not explain what 'free' means, what happens if no free results exist, whether authentication is required, or what the return shape looks like.

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 very short and front-loaded, with no wasted words. It is concise to the point of under-specification, but the phrasing itself is efficient.

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 annotations, no output schema, and 0% parameter description coverage, the description should carry much more information for an agent to call it correctly. It gives only aggregate scope and a vague 'Free only' constraint, leaving return format, usage context, and parameter behavior unaddressed.

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 are two parameters (limit and query) with 0% schema description coverage, and the description adds no meaning for either. It does not mention how limit is applied or what query matches. The description fails to compensate for the complete lack of parameter documentation.

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 aggregate resource scope: one call across components, skills, assets, and design systems. It distinguishes itself from the individual search siblings by naming all four resource types and the parallel behavior, though it does not explicitly say 'search' or name which sibling to use instead.

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 aggregate tool versus the individual aura_search_* siblings, nor any exclusions or prerequisites. The only constraint-like phrase is 'Free only,' which is ambiguous and does not tell an agent when this tool is appropriate.

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?

No annotations are provided, so the description carries the full behavioral burden. It mentions the assets are 'free' but says nothing about result shape, pagination via offset/limit, licensing terms, or rate limits, leaving a search tool with five parameters under-described.

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 short, front-loaded sentences with no filler, but the second sentence is a bare fragment ('Keywords, media_type image or video') that reads as notes rather than specification. Brevity here reflects under-specification more than discipline.

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?

A five-parameter tool with no annotations, no output schema, and zero schema description coverage needs more from the description than two sentences. Enum values, defaults, pagination semantics, and the absence of required parameters are all left unaddressed.

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 0%, so the description must compensate. It loosely covers only two of five parameters ('Keywords' for query and 'media_type' for mediaType, with a naming mismatch), and never explains sort, limit, or offset, whose values drive pagination and ordering.

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 ('Search free Aura assets') and narrows the domain to 'images and video', which separates it from aura_search_components, aura_search_skills, and aura_search_design_systems. It does not, however, distinguish itself from the broader aura_search_all sibling.

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 when-to-use guidance, no preconditions, and no routing to alternatives such as aura_search_all or aura_trending. The agent must infer from the word 'assets' alone that this is the media-specific search path.

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?

With no annotations, the description carries the full behavioral burden. It does disclose useful context — the corpus size (2,495) and that matching runs over title and description — and 'search' strongly implies a read-only operation. However, it says nothing about pagination behavior, result ordering semantics, or return shape, which leaves real gaps for a no-annotation tool.

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 verb, resource, and corpus count front-loaded, followed by the parameter hints. Nothing is padded, though the telegraphic tail ('optional category tag, sorts') reads more like schema shorthand than prose.

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?

Five parameters, no output schema, and no annotations mean the description must do more. It omits pagination (limit/offset), the meaning of the sort enum ordering, and any indication of what results contain, which is inadequate for a search endpoint with this much structured 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 description coverage is 0%, so the description must compensate and partially does: it explains that the query matches over title and description, that category is an optional tag filter, and that a sort exists. It says nothing about the limit or offset parameters, leaving two of five parameters entirely undocumented.

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 and resource ('Search free Aura UI components') and even quantifies the corpus (2,495), which is more concrete than most siblings. It does not explicitly distinguish itself from the near-identical aura_search_all or aura_search_assets, so it stops short of 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 statement of when to use this tool versus aura_search_all, aura_trending, or aura_get_component. The only implied usage is the generic act of searching, with no preconditions, exclusions, or alternative routing.

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?

With no annotations, the description carries the full burden, but it does disclose the important trait 'Metadata only' and the corpus size (725). It says nothing about pagination behavior despite limit/offset params, and nothing about ordering semantics for the sort enum.

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 tightly packed clauses with zero waste; the purpose and the metadata-only constraint are front-loaded before the sibling routing hint.

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 search tool with no output schema the description is adequate at the routing level, but with 4 completely undocumented parameters an agent must guess at query syntax and pagination behavior, leaving a real gap for invocation correctness.

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 0% and all four parameters (query, sort, limit, offset) are undocumented in both schema and description. The description adds no meaning about what query matches, what sort values do, or defaults/ranges for limit and offset, so it fails to compensate for the coverage gap.

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 (Search) and resource (Aura DESIGN.md systems), scopes it as 'free' with a count (725), and explicitly distinguishes itself from the sibling aura_get_design_system. An agent can tell search from fetch 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?

Explicitly routes the agent to aura_get_design_system when content (rather than metadata) is needed, which is the key decision point. It does not, however, state when this search is preferable to the sibling listing tools like aura_search_all or aura_trending.

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?

No annotations, so the description carries the burden. It usefully discloses that results are metadata only (no SKILL.md body), but says nothing about pagination behavior, result caps, sorting semantics, or the free/inventory scope beyond the count.

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 tight sentences, front-loaded with the action and scope, followed by the routing hint. No filler.

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 annotations, no output schema, and 0% parameter documentation, the description should carry more. It omits pagination, sort enum semantics, and result shape, leaving an agent to guess how to page or order a 187-item search.

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 0% across four parameters, and the description mentions none of query, sort, limit, or offset. The '(187)' is inventory trivia, not parameter meaning, so the description fails to compensate for the empty 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 verb and resource (search Aura agent skills) and immediately scopes it to metadata only, distinguishing it from aura_get_skill by name. An agent can tell exactly what this returns versus its sibling.

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?

Explicitly routes the agent: use this for metadata, use aura_get_skill for full SKILL.md content. That covers the key when-to-use decision against the most likely alternative, though it gives no guidance on sort/paging behavior.

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?

There are no annotations, so the description carries the full burden, and it does disclose two useful operational traits: the counts are 'Free only' and it requires 'no login,' which is genuine auth/scope context. It stops short of describing return structure or rate limits, but for a zero-argument read tool the disclosure is solidly above baseline.

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 tight fragments with no filler, and the substantive content (what it returns) is front-loaded before the 'Start here' call to action. It is efficient, though the telegraphic phrasing is slightly less polished than a fully-formed sentence.

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 zero-parameter status tool with no output schema, the description conveys what the agent will get back (health plus free counts across four categories) and the no-login/free constraints. Nothing essential for invoking it correctly is missing, though it could say more about the shape of the health/free data.

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?

The tool takes zero parameters, so per the rubric the baseline is 4. The description's enumeration of what counts are returned adds confirmation but there are no parameters whose semantics need explaining.

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 function ('catalogue health plus free counts') and enumerates the resource categories covered (components, skills, assets, design systems), which clearly separates it from the search/get/install siblings. It stops just short of a 5 because it doesn't explicitly contrast itself with those siblings by name.

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?

'Start here' is an explicit directive telling the agent this is the entry point, which is meaningful ordering guidance among 15 siblings. It does not, however, name any alternative or state when NOT to use it, so it falls short of full when/when-not coverage.

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?

No annotations are provided, so the description carries the full burden. It discloses the scope (free catalogue) and the aggregation behavior (four entity types in one response), which is genuinely useful behavioral context. It says nothing about ordering, freshness windows, read-only nature, or how 'limit' bounds the result set, leaving real gaps for a tool with zero 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?

A single well-formed sentence with no filler; the core value proposition (new and popular, aggregated across the catalogue) is front-loaded. The phrasing is slightly loose ('What is new and popular') 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 annotations and no output schema, the description is the only source of information, and it adequately conveys what categories of results come back. It does not cover the limit parameter, result ordering, or the default result size, so it is only minimally complete for an agent trying to call it correctly.

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 (limit) with 0% schema description coverage, and the description never mentions it. An agent must guess whether it caps total results, results per category, or has a default. This is the main semantic hole in an otherwise clear definition.

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 purpose: a trending/popularity feed across the free catalogue, returning top components, skills, assets and design systems. It clearly distinguishes itself from the sibling search/get tools, which are query-driven rather than popularity-driven. It stops short of naming those siblings explicitly, but the intent 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?

'in one call' implies aggregation across entity types, hinting at when to prefer this over running several searches. However, there is no explicit routing guidance versus aura_search_all or aura_recommend, nor any statement of when this is not appropriate (e.g., for a targeted query). Usage is implied rather than stated.

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?

With no annotations, the description carries the full behavioral burden. It implies some deliverable ('token starter CSS plus copy order'), but does not say whether the tool is read-only or mutating, whether it writes files, whether it requires an id/slug, or what the response contains. For a tool whose name suggests an action ('use'), this is a meaningful gap.

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 resource front-loaded after the verb. No filler, though the parenthetical 'tokens first, then markup' is cryptic rather than clarifying, slightly reducing its value.

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 and no annotations, so the description should explain the return shape and the id/slug semantics; it does neither beyond a vague mention of 'token starter CSS'. An agent lacks enough to invoke this correctly with confidence.

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?

Two parameters (id, slug) exist with 0% schema description coverage, and the description mentions neither. It does not clarify how id and slug differ, whether either is required in practice (both optional per schema), or what happens if both are omitted — so it fails to compensate for the coverage gap.

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 verb ('Apply') and a resource ('DESIGN.md system'), which is a step above a tautology, but 'apply' is ambiguous — an agent cannot tell whether this fetches/returns a design system, writes token CSS into a project, or scaffolds files. It does not clearly distinguish itself from the sibling aura_get_design_system, which likely retrieves the same artifact.

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 statement of when to use this tool versus aura_get_design_system, aura_search_design_systems, or aura_install_component. The only hint is 'copy order (tokens first, then markup)', which hints at an output sequencing convention but is not framed as a usage condition.

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