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Suganthan-Mohanadasan

Google Search Console MCP Server

genai_conversation_queries

Extract AI conversation fragments hidden in Google Search Console query data, classified into seven buckets with landing pages and monthly timeline. Identify reply artefacts, follow-ups, and AI probes disguised as regular queries.

Instructions

Surface AI-conversation exhaust hiding in your regular query data: bare replies to Google's AI ('yes', 'go on'), 'what about X' pivot follow-ups, conversational questions, AI-visibility tracker probes, and full agent prompts logged as queries. Google counts every AI Mode follow-up as a new query and folds AI Mode/AI Overviews into the web search type, so these fragments carry real impressions, positions and clicks. The dedicated Generative AI report has no query dimension; this is the only query-level AI evidence available anywhere. Classifies every match into seven buckets with landing pages, plus a monthly timeline showing when reply-artefacts first appeared on your site. Treat probe and harness buckets as machine traffic, not demand. 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
daysNoDays to analyse (default 480, the full 16 months GSC retains)
site_urlNoOverride the configured property (e.g. sc-domain:example.com)
min_impressionsNoMinimum impressions for a query to be listed (single-impression rows are evidence, not noise, so the default keeps them)
include_timelineNoInclude the monthly artefact timeline (one extra API call)
max_rows_per_bucketNoMaximum rows returned per bucket; totals always cover everything
Behavior4/5

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

With no annotations provided, the description carries full burden. It discloses that the tool makes an additional API call for the timeline, explains data limitations (Google counts follow-ups as new queries, folds AI Mode into web search type), and describes classification into seven buckets with landing pages. However, it mixes in extensive presentational instructions that are not about the tool's own behavior, which slightly detracts from focus.

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

Conciseness3/5

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

The description is verbose, containing a large paragraph and additional IMPORTANT/PRESENTATION sections that go beyond tool explanation into agent analysis and output formatting instructions. While the first part is well-structured and front-loaded, the extra sections make it longer than necessary, reducing conciseness.

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?

The description outlines the tool's output (seven buckets with landing pages, monthly timeline) but lacks details on exact return structure, error handling, or rate limits. The IMPORTANT instructions about analysis approach add some contextual guidance for the agent, but the absence of an output schema means the description should compensate more fully. Overall, it is adequate but not comprehensive.

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 baseline is 3. The description does not add significant meaning beyond what the schema already provides for parameters. It briefly relates the include_timeline parameter to 'one extra API call' but otherwise offers no extra semantics. The schema descriptions themselves are complete.

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 surfaces AI-conversation-related queries from regular query data, classifying them into seven buckets. It distinguishes itself by noting that the dedicated Generative AI report lacks a query dimension, making this the only query-level AI evidence tool. The verb 'surface' and specific resource 'AI-conversation exhaust' provide a precise purpose.

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 on when to use the tool (when query-level AI conversation evidence is needed) and explicitly mentions the Generative AI report as an alternative that cannot provide this data. It also gives caveats like treating probe and harness buckets as machine traffic. However, it does not list exclusions or compare to other sibling tools beyond that one.

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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