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Suganthan-Mohanadasan

Google Search Console MCP Server

by Suganthan-Mohanadasan
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image_impressions_no_clicks

Identify image search queries and pages with high impressions but zero clicks to fix non-converting thumbnails.

Instructions

Surfaces query and page pairs that earn meaningful image-search impressions but effectively zero clicks. The textbook 'thumbnail is not converting' pattern. Defaults tuned for image search, which runs at much higher impression volumes per page than web. IMPORTANT: Base your analysis ONLY on the data returned. Report exact numbers from the results. Do not speculate about causes (e.g. algorithm updates, competitor actions) unless the data explicitly supports it. If the data does not contain enough information to answer a question, say so clearly rather than guessing. PRESENTATION: Always present these results as a rich, interactive visualization using artifacts. Use summary cards for key metrics, colour-coded indicators for severity or change direction (green for positive, red for negative), bar charts or sparklines for comparisons, and tabbed sections to organise different categories of results. Make the output visually scannable and dashboard-like rather than plain text or raw tables. POSITION: position is an impression-weighted average over real impressions, blended across queries, devices and countries. It is not a rank-tracker rank. Deep positions are only recorded when a user actually reaches that part of the results, so values beyond page one rest on sparse data. Treat absolute positions as directional and prefer position deltas between periods when judging change.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNoNumber of days to analyse
site_urlNoOverride the configured property (e.g. sc-domain:example.com or https://www.example.com/)
row_limitNoMaximum rows to return
max_clicksNoMaximum clicks (filter to pages stuck in the impressions-no-clicks pattern)
min_impressionsNoMinimum impressions threshold

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv2.5.1
    • addedInput schema / properties / site_url
      Added value: +{
+  "description": "Override the configured property (e.g. sc-domain:example.com or https://www.example.com/)",
+  "type": "string"
+}
  2. Addedv2.4.0

TDQS

A4.3/5.0
Behavior5/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure and delivers: the POSITION section explains that position is an impression-weighted average over real impressions, not a rank-tracker rank, warns that deep positions rest on sparse data, and instructs the agent to treat absolutes as directional and prefer deltas. It also discloses the default-tuning rationale for image search and imposes analysis discipline around speculation, which is exactly the kind of context the structured fields cannot convey.

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 purpose is front-loaded in the first sentence, and the longer content is organized under clearly marked sections (IMPORTANT, PRESENTATION, POSITION) so an agent can quickly scan the critical constraints. The PRESENTATION paragraph is verbose and highly prescriptive about artifact formatting, but it dictates required agent output behavior and therefore earns its place. Slightly long overall, but well structured.

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?

With no output schema and no annotations, the description carries a heavy burden and covers most of it: result interpretation (position caveats), analysis rules, and presentation requirements are all explicit. The main gap is that the exact set of returned fields is never enumerated, though query/page pairs plus impression, click, and position metrics are inferable from the name, description, and filter parameters. For a tool of this complexity the coverage is strong; the field-listing omission is the only notable gap.

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 100%, so the baseline is 3: the schema already documents days, site_url, row_limit, max_clicks, and min_impressions with defaults and descriptions. The only parameter-level value the description adds is the rationale that defaults are tuned for image search's higher impression volumes, which lightly explains why thresholds like min_impressions=500 exist. That is marginal context rather than substantive parameter semantics.

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 opens with a specific verb and resource: 'Surfaces query and page pairs that earn meaningful image-search impressions but effectively zero clicks.' This pins the tool to a distinct pattern ('thumbnail is not converting') that separates it from image siblings like image_search_quick_wins, image_content_decay, or image_pages_overview, which target different opportunity types. There is no ambiguity about what the tool produces.

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 clear context for when the tool applies: image search analysis, with 'defaults tuned for image search, which runs at much higher impression volumes per page than web,' plus explicit analytical ground rules ('Base your analysis ONLY on the data returned... Do not speculate about causes'). The position guidance ('prefer position deltas between periods') also steers how results should be interpreted. It never names sibling alternatives or states when not to use the tool, so explicit exclusions are absent.

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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