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jurgisgavenas

search-console-mcp

search_analytics

Queries Google Search Console performance data to analyze clicks, impressions, CTR, and average position for specified URLs, dates, dimensions, and filters.

Instructions

Queries Search Console search-performance data (the Performance report).

Args: site_url: Property to query, exactly as it appears in Search Console, e.g. sc-domain:example.com or https://example.com/. start_date: Inclusive start date, YYYY-MM-DD. end_date: Inclusive end date, YYYY-MM-DD. dimensions: Group-by dimensions. Any of: query, page, country, device, date, searchAppearance. Omit for account totals. search_type: One of web, image, video, news, discover, googleNews. row_limit: Rows to return, 1-25000 (default 1000). start_row: Zero-based offset for pagination (default 0). dimension_filters: Optional list of filters, each a dict with keys dimension, operator (equals, contains, notContains, includingRegex, excludingRegex), and expression. data_state: final (default, finalized data) or all (includes fresh, not-yet-finalized data).

Returns: The raw API response, including a rows list with keys, clicks, impressions, ctr, and position.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
site_urlYes
start_dateYes
end_dateYes
dimensionsNo
search_typeNoweb
row_limitNo
start_rowNo
dimension_filtersNo
data_stateNofinal
Behavior2/5

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

No annotations are provided, so the description carries the full burden. It mentions the data_state parameter for data freshness but does not disclose rate limits, authorization requirements, error handling, or side effects. The return format is described minimally.

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 a well-structured docstring with a brief overview and parameter list. It is efficient but could be slightly more concise by integrating parameter descriptions into a narrative. However, it remains clear and organized.

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?

With 9 parameters, no output schema, and no annotations, the description covers parameter semantics and basic return shape. It mentions pagination via start_row but lacks information on error behavior, rate limits, or field-level output details. Adequate but incomplete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 0% description coverage, but the tool's description provides detailed explanations for each parameter, including examples (e.g., site_url formats), allowed values (dimensions, search_type), and default behaviors. This fully compensates for the schema gap.

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 queries Search Console search-performance data (the Performance report), with a specific verb and resource. It distinguishes from sibling tools (get_sitemap, inspect_url, etc.) by focusing on analytics data.

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, nor any when-not-to-use conditions. The description implies usage for performance data but lacks context on prerequisites or exclusions.

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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