RevenueScope: revenue-first analytics for your EC site

Top priority insights

get_priority_insights

Read-only

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_summary / get_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).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`period`	No		30d
`site_id`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`basis`	Yes
`period`	Yes
`site_id`	Yes
`insights`	Yes
`assumptions`	Yes
`limitations`	Yes
`rules_evaluated`	Yes
`presentation_hints`	Yes

TDQS

A5/5.0

Behavior5/5

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

Annotations already mark readOnlyHint=true, but the description adds substantial behavioral detail: the rule engine is deterministic and not LLM-generated, the output includes severity/number/action, and it may return fewer than 3 results. 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.

Conciseness5/5

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

The description is efficiently packed with every sentence adding value: purpose, differentiation, rule engine, output structure, parameter semantics, and edge-case behavior. It is front-loaded with the core purpose and avoids redundancy.

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 an output schema present (providing return structure), the description covers all necessary aspects: what, when, how, parameter behavior, and limitations. It is complete for an agent to correctly select and invoke the 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 coverage is 0%, but the description compensates fully: it explains that site_id is optional with OAuth, and that period accepts specific string values or a raw day count 1-365 with a default of 30d. This adds meaning far beyond the raw schema.

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

Purpose5/5

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

The description states the exact action ('Return the top 3 prioritized, pre-computed DIAGNOSES') and the resource (site insights). It also distinguishes itself from siblings by explicitly contrasting with get_summary/get_breakdown and describing the deterministic rule engine, making the tool's unique purpose unmistakable.

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

Usage Guidelines5/5

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

It provides clear when-to-use context ('what should I act on this week'), explicitly names alternatives (get_summary / get_breakdown) and explains why they differ. It also details the optional site_id and period formats, leaving no ambiguity about invocation conditions.

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

All RevenueScope: revenue-first analytics for your EC site tools Inspect

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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.

Resources

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