RevenueScope: Japanese EC RPS Benchmarks
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Ask AI for verified Japan EC RPS benchmarks (5 industries, growing). For non-analytics users.
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Tool Definition Quality
Average 4.5/5 across 7 of 7 tools scored.
Each tool has a clearly distinct purpose: AI traffic, search queries, channel/page/session breakdowns, overall KPIs, diagnostic insights, budget allocation, and site listing. There is no meaningful overlap; any potential confusion between get_summary and get_breakdown is resolved by their complementary scopes.
All tool names follow a consistent verb_noun snake_case pattern (e.g., get_ai_traffic, list_sites, suggest_budget_allocation). The naming convention is uniform and predictable.
With 7 tools, the set is well-scoped for an analytics platform covering performance, breakdowns, search, AI traffic, insights, budget, and site management. The count is neither too few nor excessive.
The tool surface covers all core functionalities expected from an analytics and benchmarking tool: summary KPIs, detailed breakdowns, search and AI traffic analysis, prioritized insights, budget allocation, and site listing. No obvious gaps are present for its stated purpose.
Available Tools
7 toolsget_ai_trafficARead-onlyInspect
Return AI-assistant (ChatGPT/Claude/Perplexity/Gemini/Copilot) traffic for the given period. mode='referred' (default) lists landing pages that received clicked AI traffic — per page × AI source: sessions, bounce rate (%, always computed; judge reliability via the sessions count), summed revenue, and last citation date (default limit 100); a view GA4/GSC cannot produce (GSC is Google-search only; GA4 lacks an AI-source breakdown). mode='gaps' returns where the site leaves AI value on the table as a ranked action list: (1) missed_citation_pages — content articles with real audience but ~0 AI traffic (push for AI citation / GEO), ranked by engagement-weighted reach; (2) under_monetized_ai_pages — pages WITH AI traffic engaging below the site's own AI norm (improve landing/CTA), ranked by AI arrivals lost below benchmark (default limit 10/list); methodology fixed in code. site_id is OPTIONAL when OAuth-authenticated. Default period is the last 30 days; pass period='today'/'7d'/'90d' or a raw day count (1-365). Scope is clicked citations only.
| Name | Required | Description | Default |
|---|---|---|---|
| mode | No | referred | |
| limit | No | ||
| period | No | 30d | |
| site_id | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses behavioral traits beyond annotations (readOnlyHint=true): describes computed metrics (bounce rate, revenue), methodology for gaps, defaults (limit, period), and scope (clicked citations). Adds value without contradicting 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?
Compact but comprehensive; information is well-organized (purpose first, then mode details, then optional params). Length is justified by complexity and number of parameters.
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 adequately covers return values for both modes (fields and types for 'referred', ranked lists for 'gaps'). Could be slightly more detailed for gaps output, but sufficient for an AI agent.
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?
Despite 0% schema coverage, the description fully explains all four parameters: mode enum with defaults, limit with mode-dependent defaults, period allowed values and syntax, site_id optionality. No missing semantics.
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?
Clearly states it returns AI-assistant traffic for a given period, with two modes 'referred' and 'gaps'. Distinguishes itself from GA4/GSC, which cannot produce this view, showing uniqueness among sibling tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit context by contrasting with GA4/GSC limitations ('a view GA4/GSC cannot produce'), guiding when to use this tool. However, does not directly compare to sibling tools like get_breakdown, missing explicit exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_breakdownARead-onlyInspect
Consolidated breakdown tool. Pick dimension: 'channel' returns per-channel sessions/revenue/RPS plus engagement (visitors, avg dwell seconds, bounce rate) and bot_excluded_count (bot sessions removed from human metrics; a channel with sessions=0 but bot_excluded_count>0 is bot-only traffic, kept so it is not mistaken for 'no traffic') and — when ad spend is connected (Path B) — spend/ROAS/saturation; plus an 'Unattributed' row (is_unattributed=true) for purchase revenue not tied to any channel, with a revenue_breakdown summary (total_event_jpy/attributed_jpy/unattributed_jpy); pass attribution_model ('last_touch' default / 'first_touch' / 'linear' / 'time_decay') to switch how purchase revenue is attributed across channels — same models as the dashboard's attribution selector; only revenue_jpy/rps_jpy change (sessions/engagement/bot/spend/ROAS are model-independent), so compare models to see e.g. how much an awareness channel gains under first_touch vs last_touch. pass filter.channel (e.g. 'google','meta','organic_search') to drill into that channel's campaigns (utm_campaign) with RPS/AOV/CVR. 'page' returns per-page pageviews/unique visitors/avg time/bounce ranked by pageviews (limit default 20, max 200; query strings stripped, bots excluded). 'session_attribute' returns the device / time-of-day (4h JST) / day-of-week (ISO) / new-vs-returning (with AOV) / country (top-15 by sessions + 'Other', ISO2 code, share_pct; from first-party session geo, 'Unknown' when IP unresolved) breakdowns in one call. site_id is OPTIONAL when OAuth-authenticated. Default period is the last 30 days; pass period='today'/'7d'/'90d' or a raw day count (1-365). filter only applies to dimension='channel'; limit only applies to dimension='page'.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| filter | No | ||
| period | No | 30d | |
| site_id | No | ||
| dimension | Yes | ||
| attribution_model | No | last_touch |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses extensive behavioral traits beyond the readOnlyHint annotation, including bot_excluded_count semantics, unattributed revenue, attribution model effects, and default period. No contradictions with 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?
The description is excessively long with no structural breaks (paragraphs, bullets). It buries essential details in a dense block, making parsing difficult. While informative, it is not concise and could be reorganized for clarity.
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 complex tool with 6 parameters and 3 dimensions, the description is remarkably complete. It covers all behavioral nuances, parameter constraints, edge cases (bot_excluded_count, unattributed), and return field details, despite lacking 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?
The input schema has 0% description coverage, so the description carries full burden. It explains every parameter in detail: dimension values with return explanations, attribution_model options and effects, period formats, filter and limit scoping, and site_id optionality. Adds significant meaning.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it is a consolidated breakdown tool and explains in detail what each dimension ('channel', 'page', 'session_attribute') returns. It distinguishes itself from sibling tools by focusing on breakdowns, making purpose 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 description provides clear context on when to use different dimensions and parameters (e.g., filter only for channel, limit only for page, attribution_model for revenue attribution). It lacks explicit guidance on when to use this tool over sibling tools, but the detailed parameter usage compensates.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_keyword_performanceARead-onlyInspect
Return search-query performance from Google Search Console for the given period. band='all' (default) returns per-query metrics — clicks/impressions/CTR/avg position/top landing page plus an estimated revenue per query (= 検索 organic RPS × clicks, a conservative estimate, 0 until the site has 検索 organic revenue), ranked by clicks (default limit 100). band='striking' returns the SEO action list: queries 'striking distance' from the top (ranking ~4-20 with real impressions) where improving a few positions yields the biggest click/revenue gain, ranked by estimated revenue opportunity (incremental clicks × search-organic RPS, default limit 10); the methodology is fixed in code. site_id is OPTIONAL when OAuth-authenticated. Default period is the last 30 days; pass period='today'/'7d'/'90d' or a raw day count (1-365). Google-search only.
| Name | Required | Description | Default |
|---|---|---|---|
| band | No | all | |
| limit | No | ||
| period | No | 30d | |
| site_id | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true, and the description adds valuable context: it details the metrics returned, explains the 'striking' band methodology, notes the conservative revenue estimate, and clarifies optional site_id under OAuth. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single dense paragraph but front-loads the main purpose. Every sentence contributes value, though the length could be slightly reduced or structured (e.g., bullet points) for easier scanning.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description fully covers return values for both bands, including metrics, methodology, and default limits. It also addresses authorization context and period defaults, making it complete for a tool of this complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With schema description coverage at 0%, the description fully compensates by explaining each parameter: band values and behavior, limit defaults per band, period accepted formats, and site_id optionality. This provides complete semantic context for correct invocation.
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 clearly states the tool returns search-query performance from Google Search Console, with specific metrics and two distinct operational modes via the 'band' parameter. It immediately communicates the core action and resource, leaving no ambiguity.
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 does not provide explicit guidance on when to use this tool versus its siblings (e.g., get_ai_traffic, get_breakdown). It implies usage for performance analysis but lacks comparisons or exclusions to help an agent choose appropriately.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_priority_insightsARead-onlyInspect
Return the top 3 prioritized, pre-computed DIAGNOSES for the site over the given period — 'what should I act on this week', ranked by revenue impact. Unlike get_site_summary / get_kpi_summary / get_channel_breakdown (which return data), this applies a deterministic rule engine over KPI period-over-period changes, per-channel RPS/ROAS/saturation, and AI-assistant referral growth, and returns ranked findings (revenue-trend swings, high-efficiency channels to scale, over-allocated low-efficiency channels, loss-making/saturated ad channels, revenue concentration risk, emerging AI traffic) — each with a severity (risk/opportunity/watch), the numbers, and a recommended action. The priority judgment is fixed in code (not LLM-generated). site_id is OPTIONAL when OAuth-authenticated. Default period is 30 days; pass period='today'/'7d'/'90d' or a raw day count (1-365). Returns fewer than 3 when fewer rules fire (no padding).
| Name | Required | Description | Default |
|---|---|---|---|
| period | No | 30d | |
| site_id | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond annotations (readOnlyHint=true, openWorldHint=false), description explains the deterministic rule engine, types of findings, severity levels, and that results are not padded (fewer than 3 when fewer rules fire). No contradictions.
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?
Efficiently packed with information, front-loaded with purpose, then differentiates, explains engine, parameters, and output behavior in a logical flow. No redundant 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?
Given 2 parameters, no output schema, the description sufficiently covers output structure (ranked findings with severity, numbers, action) and edge cases (fewer than 3 results). Complete for an AI agent.
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 0% schema description coverage, the description fully compensates: explains site_id is optional with OAuth, and period accepts 'today','7d','30d','90d' or integer 1-365 with default 30d.
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 clearly states the tool returns 'top 3 prioritized, pre-computed DIAGNOSES' ranked by revenue impact, and explicitly contrasts with sibling tools like get_site_summary that 'return data' versus this returning ranked findings.
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?
Provides explicit when-to-use ('what should I act on this week'), distinguishes from siblings, and gives parameter tips (site_id optional when OAuth, period formats, default 30d). No when-not-to is needed given the context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_summaryARead-onlyInspect
Return the full headline summary for a site and period in ONE call: the 5 KPIs (revenue, sessions, RPS, AOV, CVR) each with value AND the period-over-period change vs the previous equal-length window, PLUS a daily revenue/sessions/conversions trend, PLUS ad-spend availability (connected_channels, ad_spend_data_status, ad_spend_channels_in_period) and the Path A/B recommendation. This is what the dashboard's KPI cards + revenue-trend chart show, merged with the site's ad-spend context. Call this first when a user asks 'how is my site doing?'. site_id is OPTIONAL when OAuth-authenticated (server falls back to the primary site). Default period is the last 30 days; pass period='today'/'7d'/'90d' or a raw day count (1-365). change is a percentage for revenue/sessions/RPS/AOV and an absolute percentage-point delta for CVR. For period='today' the comparison is today-so-far vs the SAME elapsed window yesterday (e.g. midnight→now vs midnight→same-time-yesterday), so 'previous' can read below yesterday's full-day total — that is expected, not a discrepancy. ad_spend_data_status / ad_spend_channels_in_period reflect spend data ACTUALLY present in the period (consistent with get_channel_breakdown); path_recommendation is a separate last-7d recency signal and may read 'A' even when the period holds spend data. kpis.roas is the SITE-WIDE ROAS (total ad conversion value ÷ total ad spend over the period — same definition as get_breakdown's per-channel ROAS, the spend-weighted aggregate) with value/previous/change; it is null on Path A / when the period has no ad spend (ROAS is undefined with zero spend), so render it only when present.
| Name | Required | Description | Default |
|---|---|---|---|
| period | No | 30d | |
| site_id | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds extensive behavioral details beyond annotations: explains change types (percentage vs percentage-point), period='today' comparison behavior, ad_spend data status, and ROAS definition. No contradiction with readOnlyHint or openWorldHint.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is long but dense with necessary details. It could be better structured with bullet points, but every sentence adds value. It is not excessively verbose given the complexity.
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?
Given the complexity, no output schema, and annotations providing readOnlyHint, the description covers all critical aspects: return values, parameter behavior, edge cases like period='today', and ad-spend context. It feels 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 0%, but the description compensates by explaining period options and default, and that site_id is optional when OAuth-authenticated. It provides meaning 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 states it returns the full headline summary including 5 KPIs, trends, ad-spend info, and path recommendation, and explicitly says 'Call this first when a user asks how is my site doing?' which distinguishes it from sibling tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It gives clear context for when to call ('how is my site doing?') and notes that site_id is optional when OAuth-authenticated. It does not explicitly list when not to use, but the context makes it clear this is the primary summary tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_sitesARead-onlyInspect
Return the sites available to this caller: my_sites (the authenticated user's own sites with display name + domain, so the assistant can match references like "the production site" or "revenuescope.jp" without the user copying a UUID) AND demo_sites (operator-provided showcase sites for exploring RevenueScope without connecting your own). When OAuth-authenticated, prefer my_sites and default analytics tools to the is_primary=true site when site_id is omitted. When NOT authenticated, my_sites is empty and you should use a demo_sites site_id (tell the user you are analyzing a sample/example site, not their own).
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds significant behavioral context beyond annotations (readOnlyHint=true, openWorldHint=false): it explains the two categories of sites (my_sites vs demo_sites), authentication-dependent behavior, and the nature of returned data (display name+domain for matching). This helps the agent understand side effects and response structure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is moderately long but front-loaded with the core purpose. Each sentence adds unique value: definition of my_sites, demo_sites, authentication behavior, and usage advice. Could be slightly trimmed without losing meaning.
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?
Given no parameters and no output schema, the description is remarkably complete. It covers the two types of sites, behavior under different authentication states, and advice on using the returned data. No gaps remain for an agent to use this tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are zero parameters, so baseline is 4. The description adds meaning by explaining what the response contains (display name, domain) and how to use the results (matching references, default site).
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 explicitly states the tool returns sites available to the caller, naming 'my_sites' and 'demo_sites' with details like display name and domain. It clearly distinguishes itself from sibling analytics tools by being the listing tool.
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 provides clear context: when OAuth-authenticated, prefer my_sites and default to is_primary=true; when not authenticated, my_sites is empty and use demo_sites with an explanation to the user. It does not explicitly exclude alternatives, but the sibling tools are all analytics tools requiring a site_id, making this the logical prerequisite.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
suggest_budget_allocationARead-onlyInspect
Return a proposed monthly budget split across paid channels (meta/google/tiktok). site_id is OPTIONAL when the request is OAuth-authenticated. Path B (ad spend connected): precise weight = ROAS × (1 − saturation) with expected ROAS uplift. Path A (no ad spend): RPS-weighted proportional split with explicit ±20-30% caveats and a connect_incentive_message. Default period for the underlying ROAS/RPS data is 30 days; pass period='today' / '7d' / '90d' or a raw day count (1-365) to override. LLMs should pass assumptions, limitations, and connect_incentive_message through verbatim — they are hardcoded honest axis.
| Name | Required | Description | Default |
|---|---|---|---|
| period | No | 30d | |
| site_id | No | ||
| monthly_budget_jpy | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint=true, matching the non-mutating 'propose' behavior. Description details two computation paths (Path A and Path B) with formulas and caveats, plus instructions for hardcoded messages. Exceeds annotation burden.
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?
Description is dense but front-loaded with main purpose. Every sentence adds value; could be slightly restructured for readability, but efficiently conveys complex logic.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema, but description explains expected outputs (split, ROAS uplift, caveats, connect_incentive_message). Covers default period, overrides, and two paths completely.
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 has 0% description coverage; description fully compensates: explains site_id optionality in OAuth context, period default/options, and budget parameter purpose. Adds significant meaning beyond 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?
Clear statement: 'Return a proposed monthly budget split across paid channels (meta/google/tiktok)'. Distinct from sibling tools which are all get/list, making purpose 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?
Explicitly covers two paths (ad spend connected vs not), site_id optionality, period overrides, and verbatim passing of certain parameters. No direct sibling comparison needed due to different action type.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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