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

Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=true, so the safety profile is covered. The description adds genuinely new behavioral context: the comparison is drawn from a curated catalog and returns specific field groups, which matters because no output schema exists.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, no filler. The returned-value summary is front-loaded and the partial-name affordance trails it as supporting detail.

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 comparison tool with no output schema, the description covers what is compared, what comes back, and where the data comes from. Nothing an agent needs in order to call it correctly is missing.

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 both parameters already document 'partial match allowed' with maxLength constraints. The description's example strings ('Quest 3', 'PSVR2') and the partial-match restatement add marginal illustration but no syntax or format detail beyond the schema, so the baseline 3 holds.

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 (side-by-side comparison of two headsets) and names the exact fields returned (price, badge, description, retailer links) plus the data source (VR.org curated catalog). This is clearly distinguishable from every sibling, all of which are single-item get/list/search tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies the use case (compare two named headsets) and usefully notes that partial names are accepted, so an agent knows it need not resolve exact titles first. However, it never states when to prefer this over a plain lookup or what happens when a name is ambiguous or missing.

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, openWorldHint, and destructiveHint=false, so the safety profile is fully covered. The description adds only that the list is 'current' (live ranking) and sourced from VR.org, with no detail on ordering basis, list size, or refresh cadence.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single front-loaded sentence with no filler or redundancy. It is appropriately sized for a zero-parameter list 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 parameters, no output schema, and annotations covering the safety profile, the description needs only to say what comes back – which it does. Minor gaps remain around how the ranking is determined and whether games are included, given the get_top_vr_games sibling.

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, which is the baseline for a 4 – there is nothing for the description to disambiguate, and the empty schema is self-consistent with a no-argument list endpoint.

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 ('Returns ... ranked list of the top VR apps and utilities') and names the source (VR.org). It is clear what the agent gets, though it never explicitly contrasts itself with the close sibling get_top_vr_games, which shares the 'top' listing pattern.

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 or when-not-to-use guidance, and no alternative is named. The nearest siblings get_top_vr_games and get_vr_trending are not mentioned, so the agent must infer whether this covers games too or only apps/utilities.

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, destructiveHint=false, and openWorldHint=true, so the safety profile is covered. The description adds that results are 'current' and sourced from VR.org, which is useful context about freshness and provenance, but says nothing about list size, ordering, or caching.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single front-loaded sentence with no filler. Every word carries information about source, freshness, and content.

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 zero-param read tool with no output schema and annotations covering safety, the essentials are present. However, it omits how many entries are returned and how the ranking is determined, and leaves the overlap with get_top_vr_apps unresolved.

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 there is nothing for the description to clarify; the baseline for a parameterless tool applies. No parameter-level ambiguity exists.

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: returns VR.org's current ranked list of top VR games. It is clear what the tool produces, but it does not distinguish itself from the sibling get_top_vr_apps, which an agent could easily confuse with it.

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 indication of when to choose this over get_top_vr_apps, get_vr_trending, or get_vr_deals. The agent must infer the selection criteria entirely from the tool name.

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 the safety profile (readOnlyHint=true, destructiveHint=false, openWorldHint=true), and the description goes further by describing the return shape — metadata fields, canonical URL, and body HTML — which matters because there is no output schema. It omits error behavior for a bad slug and any rate/format caveats, 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?

One sentence, front-loaded with the verb and resource, followed by the returned payload. No filler, no redundancy with the 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?

With no output schema, the description carries the burden of describing returns, and it does so concretely (metadata, canonical URL, body HTML). It is nearly complete for a simple single-resource read; only edge-case behavior on a nonexistent slug is 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 100% and the schema itself supplies an example slug, so the single parameter is fully documented structurally. The description adds no slug format or validity guidance beyond what the schema already 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 and resource: returns the full content of a single VR.org original article, keyed by slug. It enumerates exactly what comes back (metadata, canonical URL, body HTML). It does not explicitly contrast itself with list_vr_originals or search_vr_news, though the singular-by-slug framing implies the distinction.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied by 'by its slug' — the agent knows it needs a slug in hand to call this. There is no explicit when-to-use/when-not statement and no alternative named for retrieving VR.org content (e.g. list_vr_originals for browsing, search_vr_news for discovery).

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, destructiveHint=false and openWorldHint=true, so the safety profile is covered. The description adds useful return-shape context (prices, badges, retailer links) but says nothing about data freshness, whether the curated set can be empty, or any rate limits on this external-facing call.

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: the return content and section list come first, the optional filter second. No filler, nothing repeated from the title, and 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 single optional-parameter read tool with no output schema, the description covers what is returned (five sections with prices, badges, retailer links) and how to narrow it. Nothing an agent needs to invoke it correctly is missing.

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 goes beyond the schema by enumerating the five valid section values (headsets, AR and XR glasses, accessories, haptics and body tracking, gaming hardware), whereas the schema only offers 'e.g. headsets'. This meaningfully helps the agent pick a legal 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?

States a specific verb (Returns) and resource (VR.org's current curated product picks) and enumerates the five sections it covers, which lets an agent separate it from siblings like compare_vr_headsets or get_vr_trending without opening any 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?

'Optionally filter to one section' implies when the parameter is used, but there is no explicit guidance on when to prefer this tool over compare_vr_headsets for headset pricing or when not to call it. 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?

Annotations already declare readOnlyHint, openWorldHint and destructiveHint=false, so the safety profile is covered. The description adds provenance (VR.org's events calendar) and ordering (soonest first), which is useful context, but it says nothing about return format or how limit affects results.

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 core purpose front-loaded and the optional parameter behavior appended. No wasted words.

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, zero-required-parameter list tool with full schema coverage and annotations carrying the safety profile, the description covers purpose, source and ordering adequately. Only minor gaps remain, such as pagination or return-shape hints, which are not critical here.

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. The description restates include_past's effect but adds no syntax or format detail beyond what the schema provides, 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?

States a specific verb and resource (returns upcoming VR/AR/XR industry events) and adds scope detail (conferences, expos, launches) plus the source calendar. It is clearly distinguishable from siblings like search_vr_news or get_vr_deals, though it does not explicitly name an alternative.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied by 'upcoming' and the include_past toggle, which tells the agent how to broaden the query. There is no explicit when-not-to-use guidance and no alternative tool is named for adjacent needs such as news or deals.

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, destructiveHint=false, and openWorldHint=true, so safety and network behavior are covered. The description adds only that the data comes from an aggregated feed; it says nothing about how 'trending' is computed, the time window, ordering, or result size, which are the traits that actually matter for a zero-parameter feed 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 sentence, front-loaded with the verb and the resource, with zero filler or redundancy. Every word 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?

With no output schema and no parameters, the description is the only source of information about the return shape, yet it does not say how many topics are returned, how they are ranked, or over what period 'trending' is measured. It is minimally adequate but leaves the agent guessing about the most operationally relevant details.

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 no parameters, so there is nothing for the description to disambiguate; baseline 4 applies. The single sentence neither contradicts nor needs to supplement an 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?

Names a specific verb (Returns) and resource (topics currently trending across the aggregated VR/AR/XR feed), which is meaningfully distinct from sibling list-style tools like get_top_vr_games or get_vr_events. It does not, however, explicitly contrast itself with those siblings, so an agent must infer the 'trending vs. top-ranked' distinction.

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 offers no guidance on when to choose this over get_top_vr_games, get_top_vr_apps, or search_vr_news, nor any exclusions or prerequisites. It simply describes the output content with no usage framing at all.

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, so safety is covered. The description adds two useful behavioral facts beyond the annotations: results are ordered newest first, and it returns summaries rather than full article bodies (implying get_vr_article is needed for content). No auth or rate-limit details, but that is a minor omission for a read-only list 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?

Two tight sentences with zero filler; the resource and content scope are front-loaded, and the optional filter is mentioned last. 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 two-parameter read-only list with no output schema, the description covers scope, content types, ordering, and return granularity. It could go slightly further by noting the absence of pagination beyond the limit cap or by pointing to get_vr_article for full text, but nothing essential to correct invocation is missing.

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 schema already documents limit (1-50, default 15) and enumerates the category values. The description only echoes that category filtering is optional, adding no syntax or semantic detail beyond the schema, so the baseline of 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (Returns summaries) and resource (VR.org's own editorial articles), and enumerates the content types (original reporting, opinion, retrospectives, guides). This clearly distinguishes it from siblings like search_vr_news and get_vr_article, which cover external news and full articles respectively.

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?

Mentions that category filtering is optional and implies a browsing/listing use case, but never states when to prefer this over search_vr_news or get_vr_article. The relationship to sibling tools is left entirely 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 establish readOnly=true, destructive=false and openWorld=true, so the safety profile is covered. The description adds real behavioral value by disclosing what is returned (per-source article counts, status, aggregate totals), which matters because there is no output schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single sentence with no filler, front-loading the resource and immediately enumerating the returned fields. 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?

With no output schema, the description carries the burden of describing the return shape, and it does so adequately (counts, status, totals). Minor gaps remain — 'status' is undefined and no ordering or pagination behavior is mentioned — but nothing essential is missing for a no-arg catalogue lister.

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 no parameters, so there is nothing for the description to disambiguate; baseline for zero-param tools is 4. No filter or pagination options exist that would 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?

The description pairs a specific verb ('Returns') with a specific resource ('the news sources VR.org aggregates') and even previews the payload (per-source counts, status, totals). It is clearly distinct from siblings like search_vr_news or get_vr_article, though it does not explicitly name those alternatives as a contrast.

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 explicit when-to-use or when-not-to-use guidance, and no sibling is named as an alternative. However, for a zero-parameter list tool that enumerates the source catalogue, the intended use is strongly implied by the resource itself.

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, openWorld, non-destructive), and the description adds real behavioral detail beyond them: every keyword must match across headline/snippet/tags/source, matching is order-independent, and headline matches are ranked first. It adds value without contradicting the annotations.

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, each earning its place: source and ordering first, then match semantics, then a practical query-writing tip. Front-loaded with the most decision-relevant information and free of 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 only three optional parameters, no output schema, and full schema coverage, the description supplies everything needed to call the tool correctly: what it returns, ordering, filter semantics, and how to shape the query. Nothing material is missing.

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 goes beyond the schema by explaining where keywords are matched (headline, snippet, tags, source) and the ranking rule that headline matches come first. It also reinforces the 'omit for latest' behavior of the query parameter.

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 ('Returns the latest VR, AR, and XR headlines'), names the source ('VR.org's live aggregated feed plus VR.org originals'), and specifies ordering ('newest first'). This clearly separates it from siblings like get_vr_article and list_vr_originals.

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 concrete usage guidance: use a few keywords rather than a sentence, and omit the query for the latest headlines — an explicit when/when-not. It doesn't name a sibling alternative or state exclusions relative to other news-ish tools, so it stops short of a 5.

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 safety (readOnlyHint=true, destructiveHint=false) and open-world scope, so the bar is lower. The description adds meaningful context beyond them: the answer is canonical/curated (not a live web lookup), and it is paired with an authoritative guide link covering buying guides and product hubs. It does not disclose failure behavior for unrecognized topics.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single front-loaded sentence that leads with what is returned, then scopes the covered topics. The example list is long but each item clarifies the accepted input domain, so 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 carries the burden of describing the return, and it does so ('canonical VR.org answer and the authoritative guide link'). For a single-required-parameter tool this is largely sufficient; the only gap is what happens when a topic falls outside the guide/hub set.

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 goes further by enumerating representative topic strings ('best headset', 'meta vr glasses', 'ar glasses', 'vr for beginners') that widen the agent's sense of accepted input beyond the two examples in the schema. That materially reduces the risk of mis-typed queries.

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: it returns a canonical answer plus an authoritative guide link for VR/AR/XR questions. This is clearly distinct from retrieval-style siblings like get_vr_article or search_vr_news. It stops short of naming any sibling directly, so it doesn't earn the explicit differentiation a 5 requires.

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 tool is for 'a common VR / AR / XR question' and the examples show the intended scope. There is no explicit when-not guidance (e.g., 'for news use search_vr_news', 'for head-to-head specs use compare_vr_headsets'), leaving the agent to infer boundaries from the example list.

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