Skip to main content
Glama
Suganthan-Mohanadasan

Google Search Console MCP Server

by Suganthan-Mohanadasan
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

image_search_quick_wins

Find image-search queries stuck at positions 4-15 with high impressions, then prioritize them by potential traffic gain if they reach position 3.

Instructions

Find image-search queries ranking at positions 4-15 with high impressions, sorted by estimated traffic gain if they reach position 3. Uses an image-search CTR baseline calibrated to the lower CTRs typical of Google Images. 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/)
max_positionNoMaximum position to include
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, the description carries the full burden and does so thoroughly. It discloses that CTR estimates use an image-search baseline, that positions are impression-weighted and not rank-tracker values, that deep positions are sparse, and that users should prefer deltas over absolute values. It also gives explicit analytical guardrails: don't speculate, report exact numbers, and state when data is insufficient.

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 longer than average, but it is well-structured with labeled sections (IMPORTANT, PRESENTATION, POSITION) and the core purpose is front-loaded in the first sentence. Some presentation details could be seen as system-level instructions, but they are relevant to how the agent should use the tool's results.

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?

The description covers purpose, data interpretation caveats, analytical disclaimers, and output presentation requirements. The main gap is that it does not explicitly enumerate the result fields or output structure, and there is no output schema to compensate. Still, it is complete enough for correct invocation and use.

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 description adds light context like 'positions 4-15' and 'high impressions,' which loosely align with max_position and min_impressions, but it does not materially enrich the parameter meanings beyond what the schema already provides.

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 ('Find'), a specific resource ('image-search queries'), and precise inclusion criteria (positions 4-15, high impressions, sorted by estimated traffic gain if reaching position 3). The image-search focus and CTR baseline clearly distinguish it from the sibling quick_wins tool.

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 clearly establishes the context: it is for image-search query opportunity analysis. It does not explicitly name alternatives or exclusions, but the target scenario is unambiguous enough that an agent can determine when to use it. It also adds interpretation guidance about position deltas.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server

Other Tools