SEO Performance MCP

GSC quick wins (positions 5-15)

gsc_quick_wins

Read-onlyIdempotent

Identify Google Search Console page-query pairs in positions 5–15 with high impressions but below-expected CTR to prioritize title rewrites and improve clicks.

Instructions

Scan GSC for (page, query) pairs sitting in positions 5-15 with non-trivial impressions and a CTR below their position-expected curve. These are the fastest title-rewrite wins.

Returns top results sorted by impressions desc. Pure GSC pull - platform-agnostic.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "gsc_quick_wins"
  }
}

Input Schema

Name	Required	Description	Default
`limit`	No
`window`	No
`max_position`	No
`min_position`	No
`min_impressions`	No

Output Schema

Name	Required	Description	Default
`wins`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Addedv0.1.2Oct 2, 2026

TDQS

A4.2/5.0

Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive, openWorld, so the safety profile is covered. The description adds real behavioral context beyond that: the ranking behavior ('sorted by impressions desc') and the fact it is a platform-agnostic GSC pull. It stops short of mentioning pagination or the effect of the window on the CTR curve.

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?

Three tight sentences, front-loaded with the core scan logic before the outcome and the return-ordering note. No filler, and each sentence carries information an agent needs.

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?

An output schema exists, so return-value explanation is unnecessary, and the description covers the filtering heuristic and sort order well. The main residual gap is the undocumented window parameter, which materially affects which pairs qualify as 'quick wins'.

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 description coverage is 0% across 5 parameters, so the description has to carry the load. It clarifies min_position/max_position ('positions 5-15') and min_impressions ('non-trivial impressions'), which is useful, but leaves the window enum (30/60/90) and the limit/cap undefined in both schema and prose.

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?

States a specific verb (Scan) and resource (GSC page/query pairs) with a precise scope: positions 5-15, non-trivial impressions, CTR below position-expected curve. It also implicitly distinguishes itself from the sibling bing_quick_wins by declaring itself a 'Pure GSC pull - platform-agnostic'.

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?

Gives clear context for use ('the fastest title-rewrite wins'), which tells the agent what decision the tool supports. However, it does not explicitly state when to prefer this over bing_quick_wins or the refresh_brief siblings, leaving the alternative selection to inference.

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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