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konkomaji

Google Marketing MCP

by konkomaji

gsc_striking_distance

Find pages ranking just below page one (positions 8-20) with the most impressions, so you can target quick traffic gains by improving their rankings.

Instructions

Queries ranking just off page one, where a small gain converts to real traffic. Position 8-20 by default.

Each row carries the page currently ranking, so the work has an obvious target. Sorted by impressions, since that is the traffic on offer.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNo
limitNo
end_dateNo
site_urlNo
start_dateNo
max_positionNo
min_positionNo
min_impressionsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.5/5.0
Behavior4/5

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

With no annotations, the description carries the burden of behavioral disclosure and does reasonably well: it reveals the position window, that each row contains the currently ranking page, and that results are sorted by impressions. It does not mention auth, rate limits, or default date handling, but its read-query framing and output schema make destructive behavior unlikely.

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 four short sentences with the core value proposition front-loaded and no wasted words. It gives the default window, explains the output granularity, and discloses the sort order, all in a compact structure.

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

Completeness3/5

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

It provides enough context to make a sensible zero-argument default call, and the output schema covers return values. However, it omits guidance on date defaults, min_impressions, site selection, and how this differs from sibling GSC opportunity reports, so an agent has limited help for choosing this tool over alternatives.

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

Parameters2/5

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

Schema description coverage is 0%, and the description only addresses min_position/max_position via 'Position 8-20 by default.' The remaining six-plus parameters—days, limit, start_date, end_date, site_url, min_impressions—are never explained, so the description compensates for only a small fraction of the parameter surface.

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

Purpose4/5

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

The description identifies a specific reporting scope: rankings just off page one, defaulting to positions 8-20, where small gains produce traffic. It clearly differentiates the tool's focus from generic GSC reports, though 'Queries' at the start is slightly ambiguous between a verb and a noun, and it does not explicitly name sibling tools.

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 phrase 'where a small gain converts to real traffic' implies the tool's intended use case, and 'Position 8-20 by default' gives a concrete selection filter. However, it never mentions alternatives like gsc_top_queries or gsc_ctr_opportunities, nor does it state when NOT to use this tool, leaving sibling differentiation 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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