Skip to main content
Glama
Suganthan-Mohanadasan

Google Search Console MCP Server

compare_web_vs_image

Compare query performance across web and image search side-by-side. Identify queries where image search impressions exceed web, using an impressions ratio to surface disproportionate volume.

Instructions

For each query, returns side-by-side performance across web and image search. Two GSC API calls joined on query, with an impressions ratio that surfaces where image search carries disproportionate volume relative to 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.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNoNumber of days to analyse
row_limitNoMaximum rows to return
min_combined_impressionsNoMinimum combined (web + image) impressions to include the query
Behavior3/5

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

The description discloses that two GSC API calls are joined on query and an impressions ratio is computed, which gives some behavioral context. However, with no annotations, it lacks transparency on critical aspects like authentication needs, rate limits, error handling, or read-only nature. The analysis instructions do not describe tool behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is overly long due to two large paragraphs (IMPORTANT and PRESENTATION) that provide user instructions and output formatting rules, which do not describe the tool itself. A concise description would be the first two sentences. This dilutes the core purpose.

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

Completeness2/5

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

Despite having no output schema, the description does not specify what fields are returned (e.g., query, web impressions, image impressions, ratio). The presentation instructions imply a visualization, but the raw data structure is missing. The tool's complexity (two joined API calls) demands more detail about the response format to be complete.

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?

Input schema has 100% coverage with clear descriptions for all three parameters. The tool description adds no additional parameter meaning beyond what the schema already provides, so baseline 3 is appropriate.

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 clearly states the tool's purpose: returning side-by-side web vs image search performance per query, including an impressions ratio. This distinguishes it from sibling tools like 'image_search_quick_wins' or 'image_keyword_overview' by emphasizing the comparison between the two search types.

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

Usage Guidelines2/5

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

No explicit guidance on when to use this tool versus alternatives (e.g., when a direct comparison is needed vs. analyzing each channel separately). The 'IMPORTANT' and 'PRESENTATION' sections focus on analysis rules and output formatting, not on usage context or prerequisites.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Suganthan-Mohanadasan/Suganthans-GSC-MCP'

If you have feedback or need assistance with the MCP directory API, please join our Discord server