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A1-x-Tech

Google Search Console MCP

Top search queries

get_top_queries
Read-onlyIdempotent

Retrieves the top search queries for a property, sorted by clicks, and returns clicks, impressions, CTR, and average position. Streamlines access to search analytics data.

Instructions

Convenience wrapper over search_analytics for the most common ask: the top search queries for a property, sorted by clicks descending (the API's default order). Each row has keys[0] = the query string plus clicks, impressions, ctr (a FRACTION 0..1) and position. Dates are calendar dates in Pacific Time, end_date inclusive; final data lags ~2-3 days. Anonymized long-tail queries are never returned. Same endpoint and quota as search_analytics — use search_analytics directly for other dimensions, pagination, fresh data or regex filters.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoHow many top queries to return (1..25000; default 100).
deviceNoOnly count traffic from this device class.
countryNoOnly count traffic from this country — ISO 3166-1 alpha-3 code, e.g. "usa".
end_dateYesLast date of the range, YYYY-MM-DD, Pacific Time, inclusive.
site_urlYesThe property EXACTLY as registered in Search Console. Two formats: URL-prefix — a full URL with scheme and trailing slash, e.g. "https://example.com/" (http/https and www/non-www are different properties), or domain property — "sc-domain:example.com" (no scheme, no slash). A mismatched value returns 403/404; list_sites shows the exact registered values.
start_dateYesFirst date of the range, YYYY-MM-DD, Pacific Time.
page_filterNoOnly count traffic to pages whose URL CONTAINS this substring, e.g. "/blog/".
Behavior5/5

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

Adds substantial behavioral context beyond the read-only/idempotent annotations: data lag of 2-3 days, Pacific Time calendar dates, ctr reported as a fraction, anonymized long-tail queries not returned, and same quota as search_analytics. No contradiction with annotations; the description enriches the operational expectations.

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?

Every sentence earns its place: purpose, output row format, timezone and lag, anonymization caveat, and alternative usage are packed into a tight paragraph. Well front-loaded and no filler.

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

Completeness5/5

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

Despite no output schema, the description covers the return row structure (keys[0] as query string, metrics), date handling, data lag, quota behavior, and exclusions. For a tool with 7 parameters and no output schema, this is unusually 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?

Schema coverage is 100% with descriptions on all parameters, so the description need not repeat them. It does not add meaning beyond the schema for parameters; the row structure note refers to output, not parameter semantics. 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 it is a convenience wrapper for retrieving top search queries, sorted by clicks descending. It explicitly differentiates itself from the sibling search_analytics tool by naming it and scoping the purpose to 'the most common ask.'

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

Usage Guidelines5/5

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

Provides explicit when-to-use and when-not-to-use guidance: 'use search_analytics directly for other dimensions, pagination, fresh data or regex filters.' This directly addresses alternatives and exclusion criteria.

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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