RevenueScope: revenue-first analytics for your EC site

Suggest budget allocation

suggest_budget_allocation

Read-only

Return a proposed monthly budget split across paid ad channels (Google Ads / Meta / TikTok Ads / Yahoo! Ads / LINE Ads etc.). site_id is OPTIONAL when the request is OAuth-authenticated. Path B (ad spend connected — any channel with spend>0 in the period): weight = ROAS × (1 − saturation) where ROAS is RS-measured revenue ÷ spend (FD-030 A-1, same as the dashboard — NOT platform-reported conversion_value). saturation は RS 自身では推定せず、広告データとして供給された場合のみ使用する (推定エンジンは W17+)。値が無いチャネルは ROAS のみで加重し効率投下上限をかけない — limitations に明記する。⚠ 配分候補は広告プラットフォーム単位 (Meta / Google Ads …) で、Instagram と Facebook のようにセッション側の粒度が細かいチャネルは所属プラットフォームへ畳んで扱う (FD-055)。 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.

Input Schema

TableJSON Schema

Name	Required	Default
`period`	No	30d
`site_id`	No
`monthly_budget_jpy`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
`path`	Yes
`site_id`	Yes
`allocation`	Yes
`assumptions`	Yes
`limitations`	Yes
`next_action`	Yes
`unallocated_jpy`	Yes
`monthly_budget_jpy`	Yes
`expected_roas_current`	Yes
`expected_roas_proposed`	Yes
`expected_roas_uplift_pct`	Yes
`connect_incentive_message`	Yes

TDQS

A4/5.0

Behavior4/5

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

The description discloses internal weighting logic (ROAS × saturation), handling of saturation (only used if supplied), channel aggregation rules, and the requirement to pass certain fields verbatim. Annotations (readOnlyHint=true) are consistent with the read-only proposal nature. No contradictions, but the technical depth could be simplified for broader agent understanding.

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

Conciseness3/5

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

The description is information-dense but not concise. It mixes purpose, algorithmic formulas, constraints, and LLM instructions in a single block. Japanese text and technical references (FD-030, FD-055) add verbosity. Front-loading the purpose is good, but overall structure could be cleaner.

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

Completeness4/5

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

Given the tool's complexity, the description covers default behavior, overrides, two usage paths, and channel aggregation. It references an output schema (not shown) and explains key constraints. Missing explicit return format details, but the output schema likely covers that. Sibling tools are all data-retrieval, so this recommendation tool stands out well.

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

Parameters3/5

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

With 0% schema description coverage, the description must add meaning. It clarifies that site_id is optional for OAuth requests, explains period options beyond the schema const values, and notes the default. However, the 'monthly_budget_jpy' parameter receives no additional context beyond its schema definition, leaving a gap.

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 clearly states the tool 'Return a proposed monthly budget split across paid ad channels', specifying the exact outcome. It distinguishes itself from sibling tools like get_breakdown or get_summary by focusing on budget allocation suggestion.

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 detailed context for use, explaining the two paths (Path A and Path B) based on ad spend connectivity, period overrides, and site_id optionality. However, it does not explicitly state when NOT to use the tool relative to siblings, and the algorithmic details may obscure the core usage guidance.

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

Try in Browser

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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

Need Help?