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FlorianBruniaux

gsc-mcp

page_analysis

Join Google Search Console and GA4 page-level data, rank by opportunity score to identify high-impact pages for optimization.

Instructions

Join GSC and GA4 data at the page level and rank by opportunity score.

GSC rows are fetched with dimensions=["page"] (already aggregated per page). GA4 organic landing pages are fetched with a high limit to avoid truncation. Pages are joined on _normalize_url. Pages that appear in only one source get None for the missing fields.

opportunity_score = log10(impressions+1)10 + engagement_rate100 + log10(conversions+1)*20

engagement_rate is derived as engaged_sessions/sessions (GA4 native formula) because ga4_organic_landing_pages does not expose it directly.

Results are sorted by opportunity_score descending, truncated to limit. hostname and country narrow the GA4 query to a specific host or country.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNo
siteYes
limitNo
countryNo
hostnameNo
property_idNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

With no annotations, the description fully discloses behavior: joining on normalized URL, handling missing pages, computing opportunity score with formula, sorting, truncating, and deriving engagement rate. This is thorough and transparent.

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 slightly long but well-structured with clear statements. Each sentence adds value, though some redundancy could be trimmed. Front-loaded with the core purpose.

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 complexity of joining two data sources and no annotations, the description covers joining logic, scoring formula, sorting, and truncation. Missing parameter details for all parameters slightly reduces completeness.

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?

Schema coverage is 0%, so description must compensate. It explains 'hostname', 'country', and 'limit' but does not cover 'site', 'days', or 'property_id' explicitly. Partial addition of meaning beyond 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 clearly states the tool joins GSC and GA4 data at the page level and ranks by opportunity score. It uses specific verbs like 'Join' and 'rank' and distinguishes itself from siblings like 'ga4_organic_landing_pages' by explaining the combination.

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 implies usage for combined page-level analysis but does not explicitly state when to use this tool over alternatives. It provides context on data sources and joining logic, which helps understand its purpose.

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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