RivalSweeper Signals

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

Annotations already declare readOnly, idempotent and non-destructive, so the safety profile is covered. The description adds genuinely non-structured context: it is 'Free and non-billable,' which is the key operational trait, and it hints that output contains per-signal freshness. It stops short of describing return structure, but goes beyond what annotations provide.

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 written sentences with zero waste, and the most important operational fact (free/non-billable, call first) is front-loaded alongside the purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a one-parameter read-only lookup with full annotations and a trivial schema, the definition is essentially complete; a brief note on what the response contains (coverage flags/freshness) would round it out, but nothing needed 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% for the single 'domain' parameter, including an example ('gymshark.com'), so the schema does the heavy lifting. The description adds no syntax or format detail beyond it, making the baseline 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?

States a specific verb ('Check') and resource ('which competitive signals RivalSweeper has for an eCommerce domain'), and the scope is immediately distinguishable from sibling analysis tools because it is a coverage/pre-flight lookup. An agent can tell what it returns 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?

Explicit routing: 'call this before any billable tool so you never pay for a domain we do not cover.' This is a clear when-to-use instruction with a stated condition and rationale, not merely implied 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?

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, and openWorldHint=true, so the safety profile is fully covered without the description. The description adds the default-scope behavior (own store vs. named rival) and the detection categories, but says nothing about return format, result volume, or pagination. A 3 is appropriate given annotations carry most of the load.

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

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

Two sentences, front-loaded with the finding categories, and no filler. The 'Ad & Deal Health:' prefix redundantly restates the title, which is minor waste but not enough to drop below 4.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a read-only finder with one optional parameter and no output schema, the description enumerates what it detects and clarifies the default target scope, which is enough for correct invocation. The absence of any return-shape hint is a minor gap given the strong annotation coverage.

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% with a single optional 'domain' parameter, so the schema already documents that omitting it targets the user's own store. The description reinforces this ('Checks your own store by default, or one named rival') but adds no format or constraint details beyond the schema. Baseline 3 is correct.

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

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

The description states a concrete verb+resource with enumerated finding types: live ads pointing to broken/out-of-stock pages, expired offers, evergreen urgency claims, and expired homepage banners. This is far more specific than a tautology and lets an agent distinguish it from general ad tools like get_cross_channel_ads_overview or get_active_promotions_and_coupons. It stops short of 5 because it never explicitly names the sibling it is not, leaving the agent to infer the boundary.

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 scope guidance 'Checks your own store by default, or one named rival' tells the agent the default behavior and the alternative target, which is useful context. However, there is no explicit when-to-use/when-not or named sibling alternative, so usage remains implied rather than directed.

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, openWorld, non-destructive, so the safety profile is covered. The description adds material value beyond that: the result is freshness-stamped and it discloses metering behavior (developer API keys: 2 credits, no data = no charge), which is genuine operational context an agent cannot get from structured fields.

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 identity of the resource in the first clause, then tacks on freshness and cost as short trailing sentences. The middle field enumeration makes the first sentence dense, but every element carries information and 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 takes on return-value explanation, listing the per-competitor fields plus the brand's own profile, and adds freshness and billing semantics. For a single-parameter read-only tool this is close to complete; only the missing sibling routing and domain-format hints keep it short of 5.

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 parameter and schema coverage is 100%, with the schema already documenting 'domain' including an example. The description only echoes 'for a domain' and adds no format or edge-case detail, 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 (find) and resource (closest competitor brands) and enumerates the exact fields returned (name, website, country, home-market flag, match tier, own DNA profile). The 'DNA competitor set' framing distinguishes it from generic competitor feeds, though it never names a sibling to contrast against.

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, when-not-to-use, or alternative routing is given. With siblings like get_competitor_moves_feed, get_predicted_competitor_moves, and get_brand_swot_analysis all touching competitors, an agent gets no help deciding among them. Use context is only barely implied by 'last-known'.

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, openWorldHint, and non-destructive behavior, so the safety profile is covered. The description adds genuinely useful context beyond that: results are 'freshness-stamped' and the credit model ('1 credit, no data = no charge') tells the agent about billing and data availability 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?

Three compact clauses that are front-loaded with the core purpose and returned fields. The trailing plan/credit note is slightly tangential but brief and useful, so overall the text 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 does the work of enumerating return fields and adds freshness and billing context, which is enough for a single-parameter read tool. It lacks explicit routing guidance among the many sibling analytics tools, which is the main remaining gap.

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% for the single 'domain' parameter, so the schema already carries the parameter meaning. The description only implies 'for a brand' via the domain, adding no syntax, format, or constraint details beyond 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?

States a specific resource (active promotions/discount codes for a brand) and enumerates the returned fields (code, discount type/value, min order, validity window). It is clear what the tool does, but it does not distinguish itself from sibling tools like get_discount_frequency_and_depth or get_most_discounted_products, so sibling differentiation is absent.

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 gives no when-to-use or when-not-to-use guidance and names no alternatives among the many discount/promotion-adjacent siblings. The only contextual note is the billing/plan detail, which is not usage selection guidance.

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, destructiveHint=false, and openWorldHint, so the safety profile is covered. The description adds genuinely useful context the annotations don't: credit cost for developer API keys and the 'no data = no charge' billing rule, plus a note that the data is uniquely RivalSweeper. It does not describe output shape beyond 'tier and highlights'.

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

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

Two sentences, front-loaded with the core purpose and pillar breakdown. The billing/credit clause is relevant but slightly dilutes the core message; overall it is efficient with little waste.

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-parameter read tool with no output schema, the description is fairly complete: it names the pillars and mentions the returned tier and highlights. It does not detail the full return structure, but annotations cover the safety profile, leaving only minor gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% with a single 'domain' parameter, so the schema already documents the input. The description adds no syntax, format, or normalization details beyond what the schema provides, matching the baseline of 3 when the schema does the heavy lifting.

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: an AI/agent-readiness score for a domain, and enumerates the six pillars (schema.org, technical access, content quality, trust, AI-specific, entity recognition). This is distinguishable from siblings like get_ai_model_citation_rate or get_how_ai_models_describe_brand, though the description never explicitly names those alternatives.

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 or mention of alternatives among the many sibling tools. The only contextual information is plan/billing-related ('included in RivalSweeper plans', '1 credit'), which addresses cost rather than when this tool is the right choice.

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, openWorld, and non-destructive, so the safety profile is covered. The description adds genuinely useful scope detail: it names the sampled models (Google Gemini, OpenAI GPT-OSS), which tells the agent the data is a limited sample. It stops short of explaining return format or freshness.

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 filler, with the core capability front-loaded and the model-coverage caveat placed second. 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 does carry the burden of describing returns, and it does so reasonably: citation rate overall and by platform, example queries, and cited URLs. Sampling limitations are disclosed. Minor gaps remain around freshness and pagination, but the essential picture is complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% and the single 'domains' parameter is fully documented in the schema, including the 50-item cap and the omit-for-all-brands behavior. The description adds nothing beyond that, 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 measurement (AI citation rate when answering shopping questions) and enumerates the breakdown dimensions: overall, by AI platform, with example queries and cited URLs. This is distinguishable from the sibling get_how_ai_models_describe_brand, which concerns descriptions rather than citations.

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 usage by clarifying what the tool returns, but never states when to reach for it versus alternatives such as get_how_ai_models_describe_brand. No exclusions or prerequisites are given, so the agent must infer the trigger.

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, openWorldHint, and destructiveHint=false, so the safety profile is covered by structured data. The description adds real content value by enumerating what the report contains (which stores succeeded, where agents got stuck, KPIs, findings), but it says nothing about freshness guarantees, result format, or size.

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 that front-loads the resource identity ('RivalSweeper's weekly race of AI shopping agents') before the colon-delimited contents list. Dense but free of filler, though the trailing list is slightly loose.

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, read-only report tool with no output schema, the description does the essential job of telling the agent what the report will contain. Enough to decide whether to call it, with only minor gaps around cadence timing and 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?

The tool takes zero parameters, so the baseline is 4; there is no parameter semantics for the description to clarify. No gap to compensate for.

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, distinctive resource: a weekly report of AI shopping agents attempting checkout across the user's store and rivals, covering completion, failure points, KPIs, and findings. This is clearly separable from AI-focused siblings like get_ai_agent_readiness_score or get_ai_model_citation_rate, though it never explicitly contrasts itself with them.

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

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

The word 'weekly' implies a recurring report cadence, but there is no guidance on when to reach for this tool versus alternatives, no prerequisites, and no exclusions. The agent must infer its own selection criteria from the resource label alone.

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, openWorld, non-destructive), so the bar is lower. The description adds genuinely new behavior: results are freshness-stamped and billing is credit-based with 'no data = no charge', which an agent needs to set expectations about cost and staleness.

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 compact sentences, front-loaded with what the tool returns, then filter behavior, then freshness and cost. Every clause carries information; 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 output schema, the description carries the return-value burden and does so ('min/max/avg/median plus recent product samples... freshness-stamped'). All three parameters are covered by the schema. Only the absence of explicit sibling routing keeps it short of complete.

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 domain, query, and limit are fully documented in the schema. The description corroborates two of them ('product-name query', 'recent product samples') but adds no syntax or format detail beyond what the schema already provides. Baseline 3 per the high-coverage rule.

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 (price statistics: min/max/avg/median plus recent product samples) scoped to a brand. It's clearly distinct from adjacent siblings like get_most_discounted_products and get_discount_frequency_and_depth, but it never names or contrasts with any sibling, so the differentiation is implicit rather than explicit.

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 mention of an optional product-name filter implies how the tool is typically scoped, but there is no when-to-use guidance, no prerequisite (e.g. brand must be tracked), and no routing to alternative pricing tools. Usage is inferable but not 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 readOnly, idempotent, openWorld and non-destructive, so safety is covered. The description adds genuine context beyond them: the weekly refresh cadence, the per-brand dimensions returned, and the 'returns no data for single-brand stores' edge case, which is exactly the kind of behavior an agent cannot infer from 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?

Two sentences, front-loaded with the tool's identity and immediately followed by the eligibility constraint. The first sentence is a dense list of output dimensions, but every element is informative and nothing is redundant.

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 by enumerating white space, moat, depth gaps, price position and discount pressure. Parameter handling is trivial (one optional field, fully documented in schema), so the only remaining gap is not naming a fallback sibling for single-brand stores.

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 parameter (owned_domain) with 100% schema description coverage, so the schema fully documents it as 'your own store domain, if you have more than one.' The description adds nothing about when or how to supply it, 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 resource and scope: 'Weekly Brand Radar for your store vs rivals' covering white space, moat, depth gaps, price position and discount pressure per brand. It is clearly distinct from sibling analyses like get_brand_swot_analysis or get_full_brand_snapshot, though it never names an alternative directly.

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 an explicit audience condition and a hard exclusion: 'For multi-brand retailers; returns no data for single-brand stores.' That is strong when-not guidance, but it does not point to which sibling to use when the exclusion applies.

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, non-destructive and openWorld behavior, so the safety profile is covered. The description adds the useful behavioral detail that results reflect change over time, but it says nothing about the time window, freshness, or coverage limits of the analysis.

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 naming the source, the resource, the temporal scope, and the four output components, with no filler. 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 single-parameter read tool with annotations covering safety and no output schema, the description conveys what is returned (S/W/O/T plus change over time). It could say more about the time horizon or whether the brand must be monitored, but it is otherwise sufficient.

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 parameter and schema description coverage is 100% — the schema already explains that 'domain' is a brand domain from the RivalSweeper account with an example. The description adds no format or sourcing detail beyond that, 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?

The description names a specific resource (SWOT analysis of a brand) plus a scope qualifier ('over time') and enumerates the four dimensions returned, which distinguishes it from sibling analysis tools like get_full_brand_snapshot or get_brand_price_statistics. It never explicitly contrasts itself with a sibling, 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?

There is no guidance on when to choose this tool over alternatives such as get_full_brand_snapshot, no stated preconditions (e.g. that the brand must already be tracked in the account), and no exclusions. Usage is only inferable from the resource 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 already declare readOnlyHint, idempotentHint, openWorldHint, and destructiveHint=false, so the safety profile is covered. The description adds no behavioral context of its own — no indication of cost, latency, rate limits, or freshness of the analysis.

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 repetition. It is appropriately sized, though it stops short of the detail a 5 would require.

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 partially cover return content by naming assortment, categories, and pricing structure, which is the minimum needed to call it. However it omits when to choose it and any sense of output shape or depth, leaving real gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% and the single required 'domain' parameter is fully documented in the schema with an example. The description contributes nothing beyond that, 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?

The description names the specific resource (a brand's product catalog) and enumerates its scope (assortment, categories, pricing structure), which distinguishes it from siblings like get_product_page_analysis and get_homepage_analysis. It lacks an explicit verb, but the retrieved content 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?

There is no guidance on when to use this tool versus the many adjacent catalog/price siblings such as get_brand_price_statistics, get_most_discounted_products, or get_product_page_analysis. The agent must infer the use case from the name alone.

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, openWorld and non-destructive, so safety is covered. The description adds useful ordering ('newest first') and the default window ('last 7 days') plus the source feed name. It says nothing about pagination, result volume, or cost, so it is a modest but not rich addition.

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 that front-loads the core purpose, then the categories, the source, and the default window. The long enumeration of categories is slightly list-heavy but still earns its place by conveying feed breadth.

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?

Annotations cover the safety profile and there is no output schema, so return values needn't be explained. However, the description leaves the 'limit' parameter's effect and any pagination/volume behavior unaddressed, which for a feed tool with only 33% schema coverage is a real gap.

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 33%: only 'domains' is described in the schema, while 'days' and 'limit' carry no description. The description compensates for 'days' by stating the 7-day default, but 'limit' is left completely unexplained in both places, so it fails to fully compensate for the low coverage.

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 ('what your rivals changed... newest first') and enumerates the signal categories covered, so an agent understands it is a broad change feed. It does not, however, distinguish itself from the very close siblings 'get_significant_competitor_changes' and 'get_predicted_competitor_moves', leaving overlap unresolved.

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 guidance is given. With siblings like 'get_predicted_competitor_moves' and 'get_significant_competitor_changes' clearly competing for the same intent, the description should route the agent but does not. Only an implicit signal ('Default last 7 days') hints at usage.

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, openWorldHint, and destructiveHint=false, so the safety profile is covered. The description adds nothing behavioral beyond that — no data freshness, no indication of what 'live' means temporally, no result-size or coverage caveats for an open-world aggregator.

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 front-loaded sentence that leads with the resource ('Cross-channel ads picture') before enumerating contents. The comma-separated list is dense but each item carries distinct informational value; no filler sentences.

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 usefully enumerates what the result contains (channel mix, gaps, insights with actions), which compensates for the missing return specification. Annotations cover the safety profile and the lone parameter is fully documented, leaving only guidance on alternatives as a gap.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% and the single optional 'domains' parameter is fully documented in the schema (format, maxItems, omission behavior). The description mentions 'your brand vs rivals' but adds no syntax or selection guidance beyond the schema, 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 the specific resource and its scope — 'cross-channel ads picture (Meta, Google and others) for your brand vs rivals' — and enumerates the dimensions returned (live/new ads, channel mix, coordinated pushes, gaps, prioritized insights). It is clearly differentiable from siblings like get_social_media_channels_and_followers or get_competitor_moves_feed, though it uses no verb and reads as a noun phrase rather than a stated action.

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 the many adjacent siblings (e.g. get_full_brand_snapshot or get_competitor_moves_feed), and no prerequisites, exclusions, or trigger conditions are given. The agent must infer usage purely from the topical 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?

Annotations already declare readOnlyHint=true, idempotentHint=true, openWorldHint=true, and destructiveHint=false, so the safety profile is fully covered structurally. The description adds only what the analysis contains, with no disclosure of data freshness, review volume, coverage window, or rate limits — useful but not rich behavioral 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?

A single tight sentence with the resource and its outputs front-loaded and zero filler. It is efficient, though as a bare noun phrase it lacks the structure of an explicit 'use this to...' statement.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a one-parameter read-only tool with full annotations and no output schema, the description is minimally sufficient: it signals the returned content (themes, sentiment, praise, complaints). It still omits any hint of output format or when the analysis is the right choice over sibling analyses, leaving a small gap given the absence of an output 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% for the single 'domain' parameter, and the schema itself gives an example (gymshark.com). The description adds no syntax, format, or constraint detail beyond that, so the schema carries the load and 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?

The description names a specific resource (a brand's customer reviews) and enumerates what the analysis covers (recurring themes, sentiment, praise, complaints), so the agent knows exactly what it gets. It does not, however, distinguish itself from adjacent siblings like get_brand_swot_analysis or get_social_media_channels_and_followers, which could also touch on customer opinion.

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 call this tool, what prerequisite (a tracked brand domain) is needed, or which sibling to prefer for related questions. Usage is only inferable from the name and the one-sentence content summary.

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 openWorldHint, so the safety profile is covered. The description adds meaningful content beyond that by specifying exactly which metrics are computed (promo days vs observed days, average and max discount %, promo types), which effectively describes the output in the absence of an 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 front-loaded sentence that leads with what the tool does and folds the window parameter in at the end. No filler and nothing redundant.

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 list the computed metrics and per-brand grouping. It is largely sufficient for a 2-param read tool, though it omits any note on the domain scoping that the schema handles.

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 50%: domains is documented in the schema, but days is bare. The description compensates by giving the window range (7-365 days) and the default (30), adding real meaning to the otherwise undocumented days 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 (get) and resource (discount frequency and depth) and enumerates the metrics produced: promo days vs observed days, average and max discount %, promo types. It is clear what the tool computes, but it never distinguishes itself from related siblings like get_active_promotions_and_coupons or get_most_discounted_products.

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 contains no when-to-use guidance, no prerequisites, and no mention of alternatives. The window/default note is parameter information, not usage context, so the agent must infer when this brand-level discount posture metric is preferable to the sibling promo and discount tools.

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, openWorldHint and non-destructive, so the safety profile is covered structurally. The description adds useful provenance ('from RivalSweeper's newsletter tracking') and scopes the output facets, but says nothing about coverage gaps, freshness, or behavior for untracked brands.

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 efficient sentence with no filler, and the key content (what patterns are returned) is front-loaded. It is a noun phrase rather than a full declarative statement, which is slightly less clear but not wasteful.

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?

There is no output schema, so the description carries the return-value burden; it does reasonably well by naming cadence, send times, themes and offers. However, it omits the required domain input, subscription/tracking prerequisites, and how empty results are signaled, which matters for an open-world data-lookup 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?

With a single required parameter and 100% schema description coverage, the schema already explains that 'domain' is a brand domain from the user's RivalSweeper account. The description adds nothing beyond this, 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 the resource (a brand's email newsletter) and enumerates the specific facets returned: cadence, send times, themes and offers. It is clearly distinguishable from sibling tools like get_social_media_channels_and_followers or get_upcoming_events_news_and_activity. It lacks an explicit verb, but the tool name and content make the intent 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?

There is no guidance on when to use this tool versus alternatives, no mention that a tracked brand domain is required, and no pointer to check_brand_data_coverage for brands lacking newsletter data. The agent must infer applicability entirely.

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, openWorld, non-destructive), so the bar is lower. The description adds genuinely new operational context not available in structured fields: the credit cost for developer API keys (15 credits) and the no-data-no-charge billing behavior. It still doesn't disclose response size or latency for what is obviously a large aggregate payload.

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 the bundle contents front-loaded; little waste. 'Included in RivalSweeper plans' leans commercial rather than operational, but it carries real entitlement information, so it 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 correctly compensates by enumerating what the bundle returns (promotions, price stats with samples, AI-readiness). For a composite tool this is close to sufficient, though it doesn't indicate how broad 'all current signals' is relative to the ~30 sibling signals, leaving payload expectations somewhat open.

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% with a single documented parameter, so the baseline is 3. The description adds only the framing that domain maps to a 'one brand-day' scope, which faintly implies a date dimension the schema does not actually expose, but contributes no format or validation detail 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 resource concretely — a bundle of all current signals for one brand-day — and enumerates the contents (promotions, price stats + samples, AI-readiness). That distinguishes it from the many single-signal siblings like get_brand_price_statistics and get_active_promotions_and_coupons, though it never explicitly frames itself as the aggregate alternative to them.

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

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

The phrase 'in one call' implies the intended use case (avoid many round-trips for a single brand), but there is no explicit when-to-use or when-not-to-use guidance relative to the individual signal tools. No prerequisites, freshness constraints, or exclusions are 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=true, idempotentHint=true, openWorldHint=true and destructiveHint=false, covering the safety profile. The description adds scope context (what the analysis assesses) but says nothing about data freshness, latency, or coverage requirements. With annotations carrying the behavioral load, this is adequate.

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 tight sentence that front-loads the resource and then enumerates the three analytical dimensions. No filler or redundancy, though the lack of any usage clause means it is efficient rather than maximally informative.

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, single-param tool with full annotation coverage, the description is roughly sufficient, and it indirectly hints at the return content. However, with no output schema, it could be clearer about what fields or format the analysis returns, leaving a modest gap.

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 and schema description coverage is 100%; the schema documents 'domain' with an example ('gymshark.com'). The description adds no parameter-level detail beyond the schema, so the baseline 3 for high-coverage schemas 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 ('analysis of a brand's homepage') and enumerates the content it covers: features/emphasis, offers/messaging, and competitive comparison. This meaningfully separates it from siblings like get_homepage_top_links or get_homepage_product_shelf_and_markdowns, though it never names those siblings 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?

There is no when-to-use or when-not-to-use guidance. Given several homepage-scoped siblings (get_homepage_top_links, get_homepage_product_shelf_and_markdowns) and broader tools (get_full_brand_snapshot), the agent is left to infer which to pick, with no routing help provided.

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 state readOnly, idempotent, openWorld and non-destructive, so the safety profile is covered. The description adds meaningful non-annotation behavior: the reporting window semantics and, crucially, the default of 90 days, which the schema does not state. It still omits any return shape, result limits, or pagination 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?

The payload is front-loaded in one dense sentence with the window constraint appended, and every clause contributes (resource, metrics, default). The mid-sentence parenthetical of three metrics is slightly cluttered but not wasteful.

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 substitutes a list of returned measures, which is helpful. However, undefined terms like 'slots' and 'best position', the absence of any result size or pagination guidance, and no indication of what the markdown comparison is measured against leave an agent guessing about the output.

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 50%: 'domains' is fully documented, but 'days' carries only min/max bounds and no prose. The description partly compensates by stating the window range and the default (90), which is genuinely new information, but it adds nothing about how the window affects the persistence metric.

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 (products on each brand's homepage shelf) and enumerates the measures returned: persistence, slots, best position, price, markdown. That is well beyond a restated title and implies a narrower scope than siblings like get_homepage_analysis or get_most_discounted_products, though it never explicitly contrasts itself with them.

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

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

Usage is only implied via the framing 'a read on what brands are pushing', which suggests the tool answers questions about sustained homepage merchandising rather than one-off discounts. There is no explicit when-to-use, when-not-to-use, or named alternative among the many homepage/discount 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?

Annotations already declare readOnlyHint, idempotentHint, openWorldHint and destructiveHint=false, so safety is covered. The description adds genuine behavioral context beyond that: the top-level 'comment' flag when pricing/stock is not captured, and the credit/charging semantics (2 credits, no data = no charge).

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 grouping, then per-link fields, then caveats and cost. The long field enumeration is dense but earns its place because no output schema exists; no filler sentences.

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 return-value disclosure itself: group order, per-link fields (title, vendor, price, currency, stock, URL status, rating, position, anchor text, update), and the comment caveat. An agent has enough to invoke and interpret 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?

Only one parameter (domain) and schema description coverage is 100%, with the example 'connox.com' already in the schema. The description adds no syntax or format information beyond the schema, 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+resource ('A brand's current homepage top links') and quantifies scope (up to 10) plus how results are grouped (Products, Categories, Content). An agent can tell it returns links, not an analysis. It does not explicitly distinguish itself from close siblings like get_homepage_analysis or get_homepage_product_shelf_and_markdowns, which keeps it from 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 description explains what is returned and the billing model but gives no when-to-use/when-not guidance and never names an alternative tool. For a niche homepage endpoint with several homepage-adjacent siblings, the routing guidance is a clear gap.

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, openWorld and non-destructive, so safety is covered. The description adds genuinely new context not in structured fields: the data source is sampled from Google Gemini and OpenAI's open-weight GPT-OSS models, which shapes how a consumer should interpret the results. It stops short of disclosing freshness, sampling size, or rate limits.

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 dense sentence plus a short sourcing sentence; front-loaded and mostly waste-free. Enumerating all seven aspect values duplicates the schema enum, which is the only mild bloat.

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 never indicates what the response contains (per-aspect text? scores? sources?) beyond the aspect names. For a read-only analytical tool this is acceptable but leaves a real gap in knowing 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 the aspect enum is fully documented in the schema, so the description's restatement of the seven aspect values is redundant. It adds nothing about the domain parameter or how aspect values differ semantically. 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+resource: how LLMs describe a brand when asked shopping questions, broken out by aspect. The domain/aspect framing distinguishes it from near-siblings like get_ai_model_citation_rate or get_ai_agent_readiness_score, though it never names those alternatives directly.

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 mention of alternatives among the many AI-visibility tools (citation rate, readiness score, shopping agent checkout race). Usage must be inferred from the aspect list.

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, openWorld, so the safety profile is covered. The description adds genuine behavioral context by disclosing that output includes deltas versus the last check, but says nothing about freshness/lag, rate limits, or account requirements.

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 with zero filler, front-loaded with the returned fields and ending on the interpretive payoff. It is slightly overloaded with comma-separated clauses but every clause contributes 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?

There is no output schema, so the description carries the burden of describing returns and does so reasonably well: role breakdowns, change vs last check, top titles, and added/removed jobs. For a single optional-parameter read tool, that is close to complete, though it omits default time window and result limits.

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?

With 100% schema description coverage, the sole parameter (domains) is already fully documented with an example and omit-for-all behavior. The description does not mention the parameter at all, so it adds nothing beyond the schema — 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 resource precisely (open roles per brand, broken out by department and location, plus job-title and add/remove deltas) and frames it as a competitive-investment signal. It is clearly distinguishable from siblings, none of which cover hiring. It stops short of a clean verb-first statement like 'List job openings', but the content 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?

The trailing clause 'an early read on where a rival is investing' implies when the tool is useful, but there is no explicit when-to-use/when-not guidance and no alternative sibling is named. An agent can infer intent but gets no routing instructions 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?

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint, so the safety profile is covered. The description adds the 'right now' freshness semantics and the returned price/full-price/discount fields, which is genuine extra context, but it says nothing about ranking determinism or result counts.

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; the core resource is stated before the qualifying clauses. Slightly dense with parentheticals but every clause carries information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description usefully names the return fields, and the required domains parameter is covered by the schema. However it omits the limit default/cap behavior and gives no guidance on combining multiple domains across accounts, leaving gaps for a read tool with three parameters.

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 67% and the enum for sort is already documented ('Default percent'). The description's 'by % or amount ... or its newest discounts' maps loosely onto those enum values without adding syntax or defaults beyond the schema, and the limit parameter is undocumented in both places. Baseline 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?

States a specific verb+resource ('a brand's most-discounted products right now') and enumerates the returned fields (price, full price, discount), which separates it from analytics siblings like get_discount_frequency_and_depth. It does not explicitly name which sibling to use alternatively, so it stops short of full differentiation.

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 phrase '(by % or amount) or its newest discounts' hints at the sort modes but never says when to pick this over get_active_promotions_and_coupons or get_discount_frequency_and_depth. No prerequisites, no exclusions, no explicit 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?

Annotations already cover the safety profile (readOnlyHint, idempotentHint, destructiveHint=false, closed-world), so the description's value is in what it adds: the default pending filter and the shape of each record (priority, status, owner, due date). It does not mention pagination or the 100-item cap implied by the limit parameter, which is the main remaining 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?

One front-loaded sentence names the resource, parenthetical examples clarify intent, and a short trailing clause states the default. No filler, though the examples and the field list make it slightly denser than the minimum needed.

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 list tool with no output schema, the description compensates by enumerating the returned fields and the default filter. The only meaningful omission is the result-count behavior controlled by limit, which leaves an agent guessing about paging.

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 50%: the status parameter is documented with its default enum list, while limit (1-100) is undocumented in both schema and description. The description's 'Default: pending items' mostly restates the status default already in the schema and adds no syntax or cap information for limit.

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 (action items RivalSweeper raised for your team) and concretizes it with two examples (fix a broken ad landing page, respond to a rival promotion). It implicitly separates itself from data-inspection siblings like get_competitor_moves_feed by framing these as assigned team to-dos, though it never names an 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?

'Default: pending items' tells the agent what it gets without arguments, which is genuine usage context, but there is no guidance on when to call this versus the many sibling analysis tools, and no stated 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?

Annotations already establish readOnly/idempotent/non-destructive/closed-world, so the bar is lower. The description adds genuine value beyond them: the tool is free, it is scoped strictly to the caller's own data ('only ever returns your own usage'), and it explains the two consumption models (plan-based vs. prepaid API credit) and the look-back window. It stops short of describing return structure or freshness, so not 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?

Three tight sentences, front-loaded with the core purpose and scoping statement. Dense with facts and no filler, though the API-key clause is slightly compressed and could be marginally clearer with a touch more 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?

With no output schema, the description carries the return-value burden and does so fully by enumerating what the caller gets back for both signed-in and API-key modes. Combined with annotations covering the safety profile and a fully documented parameter, nothing needed 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% and the single 'days' parameter is already documented in the schema with its default (30) and bounds. The description restates the look-back window and default but adds no syntax or behavior beyond that, 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+resource ('Your own account status') and enumerates exactly what it returns: plan, tool calls used vs. plan limit, queryable domains, and for API-key users the prepaid balance and per-tool call/credit consumption. Clearly distinguishes itself from the brand-analysis siblings, none of which touch account state.

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 makes the use case unmistakable (check plan, quota, and domain access) and notes it is 'Free; only ever returns your own usage,' which signals when it is appropriate. No explicit when-not or named alternative is given, but no sibling tool serves this purpose, so the guidance is effectively complete.

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, openWorld), so the description is free to add operational behavior: read-only sitemap-derived data, newest-first ordering, a 7-day default window, and a 50-row default with a 200 cap. It stops short of describing errors or pagination 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?

Two dense, front-loaded sentences with no filler; scope, source, and ordering come first and the defaults/limits follow. Nothing extraneous, though the second sentence compresses several distinct constraints into one clause.

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-required-parameter, read-only list tool with no output schema, the description plus schema gives an agent enough to call it correctly: source, ordering, defaults, caps, and the domains filter. Return-field detail (type/tags shapes) and the 90-day ceiling are the only 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 only 33% (only `domains` is described), so the description has to carry `days` and `limit`. It supplies the default window (7 days), default row count (50), and the 200-row ceiling, which meaningfully compensates — though it omits the 90-day ceiling that only appears in 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 verb+resource — newly published products and pages — plus the data source (sitemaps), grouping (per brand), sort order (newest first), and returned attributes (type and tags). No sibling is named, but the resource is unique among the sibling list, so confusion risk is low.

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

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

Usage is implied by the 'newly published' framing and the default 7-day window, but the description never states when to prefer this over alternatives such as get_most_discounted_products or get_significant_competitor_changes, nor any preconditions.

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, openWorldHint, and destructiveHint=false, so safety concerns are covered. The description adds useful behavioral context beyond them, including the weekly refresh cadence and the fact that results carry confidence, underlying evidence, and a short narrative. It stops short of describing pagination or how much history is retained.

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 conveys the source, cadence, scope, examples, and return payload with no wasted words. Nothing needs re-reading or trimming.

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 zero parameters and no output schema, the description carries the burden of describing results and does so well (confidence, evidence, narrative). Coverage of scope ('each tracked brand') is present, though it is vague about time horizon and whether predictions are returned for all brands unconditionally.

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-4 case. The description correctly implies no input filtering by framing the output as covering 'each tracked brand,' consistent with 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?

The description names a specific verb (predicts), resource (each tracked brand's likely next moves), source (RivalSweeper's weekly predictions), and concrete examples (a sale, a launch). It clearly states scope and return content, but it never distinguishes itself from closely named siblings like get_competitor_moves_feed or get_significant_competitor_changes, leaving the agent to infer which one to pick.

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, when-not-to-use, or naming of alternatives, despite the presence of several plausible sibling tools covering competitor moves. The weekly cadence is mentioned but framed as a property of the data, not as routing guidance.

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, openWorldHint, and destructiveHint=false, so the safety profile is covered. The description adds the analysis dimensions, but does not disclose depth, freshness, or whether the brand must already be tracked in the account.

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 identifies the analysis subject first and the coverage dimensions second. No filler, though the parenthetical list is dense rather than purely economical.

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?

There is no output schema, so the description partially compensates by naming what the analysis covers. However, it says nothing about the return structure, depth, or prerequisites for a domain, leaving an agent with an incomplete picture of what it will receive.

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?

Only one parameter, and schema description coverage is 100% – the schema already explains that 'domain' is a brand domain from the RivalSweeper account with an example. The description adds nothing about the parameter, 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 the resource (product pages) and enumerates the analyzed dimensions (content, imagery, trust signals, offers, cross-sell), which is far more specific than a tautology. It implicitly separates itself from get_homepage_analysis and get_catalog_analysis by scoping to product pages, but never names those siblings 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?

No when-to-use guidance and no alternatives referenced. An agent choosing between this, get_homepage_analysis, and get_catalog_analysis gets no routing signal beyond the phrase 'product pages'.

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, openWorld, non-destructive), and the description adds genuinely new context: the >10% threshold that defines 'significant', the chronological alert-feed shape with summary, entitlement via RivalSweeper plans, and the billing rule (2 credits, no data = no charge). Those are facts the agent cannot derive from annotations or schema. It does not mention rate limits or auth mechanics beyond plan inclusion.

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 sentences, front-loaded with the qualifying thresholds, then return shape, then window and billing. Nothing is redundant, though the plan/credit sentence is commercial metadata rather than invocation-relevant 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 two-parameter read tool with no output schema, the description supplies the alert-feed return shape, the significance thresholds, and the look-back window bounds. The main remaining gap is disambiguation from the near-identical moves/predictions siblings.

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 schema already documents both 'domain' (with example) and 'days' (min 1, max 90, default 7). The description restates the window range and default without adding format or semantics beyond it. Baseline 3 applies when the schema carries the parameter burden.

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 resource (significant competitor changes) and enumerates exactly what qualifies: price moves >10%, new promotions, stock-status flips. That threshold specificity makes the tool's output scope unambiguous. It stops short of naming how it differs from the closely-related siblings get_competitor_moves_feed and get_predicted_competitor_moves, so it is clear but not fully differentiated.

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 says what the tool surfaces but never states when to reach for it instead of get_competitor_moves_feed, get_predicted_competitor_moves, or get_discount_frequency_and_depth. The only 'guidance' is the window parameter and plan/credit notes, which are availability facts rather than usage 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?

Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and destructiveHint=false, so the safety profile is covered. The description adds no behavioral context beyond the field list: no data freshness, no coverage caveats, no indication of what 'posting cadence' means operationally.

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 short, front-loaded sentence with zero filler. It is efficient, though the telegraphic fragment style leaves no room for the usage or behavioral cues that would make it more actionable.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a read-only, one-optional-param tool with no output schema, the description is minimally sufficient: it enumerates the returned entity types. It stops short of explaining what the caller gets per channel (handles, counts, cadence granularity), which is the main remaining gap.

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 optional parameter and schema description coverage is 100%, with the schema already explaining the domain format and the omit-for-all behavior. The description contributes nothing about the parameter, 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 the resource and its fields precisely: social channels, followers, and posting cadence per brand. It is a noun phrase rather than a verb+resource statement, and it does not differentiate itself from look-alike siblings such as get_email_newsletter_insights or get_cross_channel_ads_overview, but an agent can still tell what data comes back.

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 or when-not-to-use guidance. Nothing tells the agent how this differs from the newsletter, cross-channel ads, or full brand snapshot tools, and the only scoping hint (omit domains for all tracked brands) lives in the schema, not 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, openWorldHint and destructiveHint=false, so the safety profile is covered. The description adds one useful behavioral note – the data is 'detected by RivalSweeper', implying detection-based coverage that may be incomplete – but says nothing about return format or whether a brand with no detected stack returns empty.

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 tight sentence with zero waste, and the resource is front-loaded. It is grammatically a noun fragment rather than a full statement, which slightly weakens the framing but not the efficiency.

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 some burden for return values; it conveys the content (services per brand) but not the shape (e.g. per-domain grouping). It is adequate for a simple one-optional-param lookup but leaves the agent guessing at output structure.

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%: the single 'domains' parameter is fully documented in the schema, including the 'Omit for all your tracked brands' default behavior. The description adds no parameter-level meaning beyond this, 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?

Names a specific resource – the software services/technologies each brand runs, with example categories (analytics, marketing, payments, reviews, search). An agent can tell this apart from siblings like get_homepage_analysis or get_social_media_channels_and_followers. It does not explicitly name a sibling it is distinct from, 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?

The description states what data is returned but never says when to reach for this tool versus alternatives, nor any prerequisite (e.g. needing tracked brands). No when/when-not guidance is present.

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, idempotent, open-world, non-destructive behavior, so the safety profile is covered. The description adds useful context beyond them — the 7-day news window and the country/per-brand scoping — but says nothing about return format, volume, or freshness of the non-news buckets.

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, front-loaded with the 'dashboard agenda' framing and then listing the three components in parallel. No wasted clauses, though the run-on structure packs three distinct features into one breath.

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-required-param aggregation tool with no output schema, the description adequately conveys what content comes back. The main omission is any sense of response shape or relative freshness across the three buckets, which an agent would otherwise need to learn by calling it.

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 domains parameter is fully documented in the schema, including the omit-for-all-brands behavior. The description reinforces the per-brand scope but adds no syntax or format detail beyond what the schema provides, 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?

Names three concrete content buckets — upcoming holidays/retail events by shipping country, news mentions (7 days), and recent catalog activity per brand — which is far more specific than the title alone. It does not, however, distinguish itself from related siblings such as get_competitor_moves_feed or get_significant_competitor_changes, so an agent must infer the boundary.

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?

'Your dashboard agenda' hints that this is an overview/aggregation call, but there is no explicit when-to-use guidance, no prerequisites, and no alternative named. With ~30 sibling analytics tools, the absence of any routing advice is a real gap.

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 description doesn't need to. It adds value by disclosing the return content—own sites, rival brands, and status—which is meaningful for a read-only listing with no output schema. It omits return format or ordering 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?

Two short sentences with zero waste: the first defines the returned set, the second states the use case. Content is front-loaded with the resource 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 explaining returns, and it does identify what comes back (own and rival brands plus status). No parameters to document. It could say more about the status field's possible values, but the essentials for calling it correctly are present.

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 disambiguate and the schema is trivially complete. Baseline 4 applies for a parameterless tool.

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 precisely (your own sites plus monitored rival brands, with status) and scopes it to the user's account. It is distinct from every sibling, none of which list tracked brands. It stops short of an explicit action verb, relying on the tool name for that.

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

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

It gives a clear when-to-use: 'use it to see which domains the other tools can answer for,' which routes the agent from discovery to the analysis tools. It does not state any exclusions or name a concrete alternative, but the routing intent is unambiguous.

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 is a non-destructive, open-world, non-idempotent submission; the description goes further with the operationally important fact that it is 'Free and non-billable' and that fulfillment is demand-prioritized (i.e., no immediate data return). It stops short of saying what the caller receives back or expected turnaround.

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 short sentences, front-loaded with the core action, then the routing condition, then the cost caveat. No filler and nothing repeated from the title or schema.

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 request-submission tool with no output schema, the description covers the essentials: what to submit, when, and that it costs nothing. It leaves a small gap around what the response confirms (acknowledgement vs. request ID) and how/when the requested signal becomes available.

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 domain, signal, and note are already fully documented including example signal values. The description only restates that the request is scoped to a domain and adds no syntax, format, or constraint detail 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 description states a specific verb and resource: 'Tell RivalSweeper exactly what signal you need for a domain,' clarifying it is a demand/request submission rather than a retrieval. It ties itself to a named sibling (check_brand_data_coverage), but does not distinguish itself from the other request-style sibling, request_new_brand_monitoring.

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

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

It gives two concrete triggers: 'when check_brand_data_coverage shows a signal is missing' and 'when you need data we do not yet track.' That is a clear when-to-use with a named upstream diagnostic tool, but there are no explicit exclusions or an alternative to prefer for adjacent cases (e.g., a brand not yet tracked).

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=false, idempotentHint=false, destructiveHint=false, and openWorldHint=true, so the safety profile is covered. The description adds genuinely new behavioral context: the cost model (free), the authorization prerequisite (plan or positive prepaid balance), and expected latency (first data in 24-72h). It omits what happens on a duplicate request for an already-monitored domain, which is the one behavior an agent would still want.

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 short sentences, front-loaded with the action, then the gating condition, then the timing expectation. Each sentence adds information, though "Free" partially duplicates the title's "(free, gated)" and could be trimmed.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a one-parameter write tool with no output schema, the description covers the essentials: what happens, what is required to succeed, and how long results take. Annotations already supply the mutation/idempotency profile. The only material gap is duplicate-request behavior for an already-tracked domain.

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 ("Domain to start monitoring"), so the schema already carries the load. The description adds no format guidance beyond it, such as whether subdomains, paths, or full URLs are accepted. Baseline 3 is appropriate when the schema documents the only 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?

The description states a specific verb and resource: "Ask RivalSweeper to add a domain to monitoring." An agent can immediately tell this is a write/onboarding action rather than a read, distinguishing it from siblings like list_my_tracked_brands or check_brand_data_coverage. It stops short of naming a sibling it should be preferred over (e.g., request_missing_data_signal), so it is clear but not fully differentiated.

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 real selection context: the tool is free but gated behind an active plan or a developer account with a positive prepaid balance, which tells the agent when the call will actually succeed. It does not, however, state when-not to use it or which alternative handles related needs, so it is clear context without exclusions.

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