RevenueScope: revenue-first analytics for your EC site

Search keyword performance

get_keyword_performance

Read-only

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). Each row also carries the period-over-period change vs the previous equal-length window: clicks_change (traffic) and est_revenue_change (money), both % deltas (null = the query is NEW, i.e. had no clicks/revenue last period — render as '新規', not 0%). Comparing the two surfaces RS's signature insight — e.g. clicks +74% but est_revenue −21% means traffic grew while money fell, something GA4/GSC cannot show side by side. 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.

Input Schema

TableJSON Schema

Name	Required	Default
`band`	No	all
`limit`	No
`period`	No	30d
`site_id`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`band`	Yes
`rows`	Yes
`basis`	No
`period`	Yes
`site_id`	Yes
`warning`	No
`criteria`	No
`assumptions`	No
`limitations`	No
`rps_search_jpy`	No
`revenue_estimate_basis`	No

TDQS

A4.5/5.0

Behavior5/5

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

The description adds substantial behavioral detail beyond the readOnlyHint annotation: revenue estimation formula (search-organic RPS × clicks), period-over-period change semantics with null meaning new query, site_id optionality under OAuth, and fixed methodology for striking band. It even explains how to interpret the change metrics with an example. 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.

Conciseness4/5

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

The description is dense but every sentence carries substantive information. It is a single block without paragraph breaks, which makes it harder to scan, but it is not verbose or redundant. The front-loading of the main purpose is effective.

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

Completeness5/5

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

With four parameters, an output schema, and annotations, the description covers all necessary context: default periods, limits, auth-related site_id, metric definitions, change semantics, and the 'Google-search only' scope. It leaves no obvious gaps for a complex tool.

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

Parameters5/5

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

Schema description coverage is 0%, so the description bears the full burden for parameter semantics. It explains band values and their behaviors, limit defaults (100 for all, 10 for striking), period accepted formats, and site_id optionality. This fully compensates for the schema's lack of descriptions.

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

Purpose4/5

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 for a period, and distinguishes between the 'all' and 'striking' bands. It is specific about the verb and resource, but does not explicitly differentiate from sibling tools like get_ai_traffic or get_breakdown, 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.

Usage Guidelines4/5

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

The description provides clear context for when to use each band (e.g., 'striking' returns the SEO action list for queries near the top) and gives an example of the insight from comparing metric changes. However, it does not explicitly state when to use this tool over alternatives or list exclusions.

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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TDQS

A4.6/5.0

Disambiguation5/5

Each tool has a distinct purpose: AI traffic analysis, multi-dimensional breakdown, competitor SEO snapshot, content playbook, keyword performance, page trend, priority insights, summary, site listing, and budget allocation. Overlapping domains like search performance are clearly separated by focus (query-level vs page-level vs competitor).

Naming Consistency5/5

All tool names follow a consistent verb_noun snake_case pattern (e.g., get_ai_traffic, get_breakdown, list_sites). The verbs are uniformly descriptive ('get', 'list', 'suggest'), and the naming style is predictable and clean.

Tool Count5/5

With 10 tools, the server is well-scoped for a comprehensive analytics platform. Each tool covers a critical area (summary, traffic sources, breakdowns, search performance, competitor analysis, content actions, budget allocation) without unnecessary bloat.

Completeness5/5

The tool surface covers all major aspects of revenue-first e-commerce analytics: overall KPIs, AI traffic, channel/page/session breakdowns, search keyword and content performance, competitor insights, trend analysis, priority diagnoses, and budget recommendations. No obvious gaps for the stated purpose.

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