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gsc-mcp-server

by Klartika
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Python
Hybrid

get_advanced_search_analytics

Fetch Google Search Console analytics with filters, dimensions, sorting, and pagination. Analyze clicks, impressions, CTR, and position over custom date ranges.

Instructions

Get advanced search analytics data with sorting, filtering, and pagination.

Args:
    site_url: Exact GSC property URL from list_properties (e.g. "https://example.com/" or
              "sc-domain:example.com"). Domain properties cover all subdomains — use the
              domain property as site_url and filter by page to analyze a specific subdomain.
    start_date: Start date in YYYY-MM-DD format (defaults to 28 days ago)
    end_date: End date in YYYY-MM-DD format (defaults to today)
    dimensions: Dimensions to group by, comma-separated (e.g., "query,page,device")
    search_type: Type of search results (WEB, IMAGE, VIDEO, NEWS, DISCOVER)
    row_limit: Maximum number of rows to return (max 25000)
    start_row: Starting row for pagination
    sort_by: Metric to sort by (clicks, impressions, ctr, position)
    sort_direction: Sort direction (ascending or descending)
    filter_dimension: Single filter dimension (query, page, country, device). Use 'filters' instead for multiple filters.
    filter_operator: Single filter operator (contains, equals, notContains, notEquals)
    filter_expression: Single filter expression value
    filters: JSON array of filter objects for AND logic across multiple dimensions. Overrides
             filter_dimension/filter_operator/filter_expression when provided. Each object must
             have 'dimension', 'operator', and 'expression' keys. Valid dimensions: query, page,
             country, device. Valid operators: contains, equals, notContains, notEquals.
             Example: [{"dimension":"country","operator":"equals","expression":"usa"},
                       {"dimension":"device","operator":"equals","expression":"MOBILE"}]
    data_state: Data freshness — "all" (default, matches GSC dashboard) or "final" (confirmed data only, 2-3 day lag)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
filtersNo
sort_byNoclicks
end_dateNo
site_urlYes
row_limitNo
start_rowNo
data_stateNo
dimensionsNoquery
start_dateNo
search_typeNoWEB
sort_directionNodescending
filter_operatorNocontains
filter_dimensionNo
filter_expressionNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Tool Definition Quality

A4.8/5.0
Behavior5/5

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

With no annotations provided, the description carries the full burden and excels. It discloses critical behavior: the filters parameter overrides the single-filter parameters, data_state semantics (final data has a 2-3 day lag), row_limit max of 25000, pagination via start_row, and defaults for dates. This goes well beyond what a schema alone would convey.

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?

The description is long but every sentence adds substantive value. The Args-style list is front-loaded with the primary resource (site_url) and proceeds logically. Given 14 parameters, the length is justified and there is 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?

The tool is complex with 14 parameters and an output schema, so the description does not need to explain return values. It covers all parameters, defaults, overrides, and edge cases, making it fully self-contained. The provided output schema handles the return structure.

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?

Schema description coverage is 0%, so the description is the sole source of parameter meaning. It explains every parameter with format examples, valid values, defaults, and relationships (e.g., filter_* vs. filters). The filters example with JSON array is especially helpful.

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 opens with a specific verb+resource: 'Get advanced search analytics data with sorting, filtering, and pagination.' It clearly distinguishes this tool from the sibling get_search_analytics by emphasizing advanced capabilities. The name and description align.

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 (for advanced analytics with sorting/filtering/pagination) and includes a practical usage tip about domain properties vs. filtering by page. However, it does not explicitly name alternatives or state when NOT to use this tool (e.g., simple queries might use get_search_analytics).

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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