Google Search Console Audit MCP

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gsc_quick_wins

Read-onlyIdempotent

Identify queries near the top that gain more clicks when pushed to a target position, helping you prioritize SEO actions with clear payoff.

Instructions

Queries close enough to the top that a push would pay off.

Ranked by the clicks each query would gain at target_position, not by impressions: a big query already near its ceiling is not an opportunity. The meta reports the thresholds that were applied.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`days`	No	Rolling window length ending today.
`period`	No	Named range: last_7_days, last_28_days, last_3_months, last_month, this_month, last_16_months.
`date_to`	No	End date YYYY-MM-DD. Defaults to today.
`no_cache`	No
`site_url`	Yes	`sc-domain:example.com` for a domain property, or `https://example.com/` with the trailing slash for a URL-prefix property. Call gsc_sites if unsure.
`date_from`	No	Start date YYYY-MM-DD. Overrides period and days.
`row_limit`	No	Rows returned. Default 100, capped at 1000.
`thresholds`	No	Override the adaptive thresholds: min_impressions, min_clicks, high_visibility_impressions, ctr_curve.
`max_position`	No	Ignore queries below this position.
`min_position`	No	Ignore queries above this position.
`response_format`	No	`tsv` (default, compact) or `json`.	tsv
`target_position`	No	Position to size the opportunity against.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Addedv3.0.1Aug 28, 2026

TDQS

A4.2/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, covering safety. The description adds valuable behavioral details: ranking by potential clicks rather than impressions, and the existence of adaptive thresholds reported in the meta. This goes beyond what the annotations provide.

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 two concise sentences. The first sentence clearly states the core function, and the second adds the critical ranking nuance. Every word earns its place; no fluff or redundancy.

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 output schema exists, return values need not be explained. The description covers the key behavioral aspects, but it does not mention how conditions like threshold overrides interact with defaults, or any caveats about caching. Still, for a read-only analysis tool, it is sufficiently complete.

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

Parameters4/5

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

Schema coverage is 92%, so most parameters are already documented. The description adds meaning by explaining the role of target_position in the ranking logic, which is not obvious from the schema alone. This supplements the parameter semantics usefully without needing to repeat schema details.

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 a specific purpose: finding queries close enough to the top that a push would pay off. It clearly differentiates from other GSC tools by explaining the ranking logic (based on potential clicks at target_position, not impressions), which distinguishes it from similar tools like gsc_ctr_gaps or gsc_query.

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

Usage Guidelines3/5

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

The description implies the use case (identifying quick-win queries worth pushing) but does not explicitly state when to use this tool versus alternatives, nor does it mention any exclusions. The context is clear enough, but explicit routing to a sibling tool like gsc_query for general query analysis would improve it.

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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