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surendranb

Google Search Console MCP Intel Engine

by surendranb

get_search_analytics

Retrieve search analytics data from Google Search Console, including clicks, impressions, CTR, and position, with options to filter by dimensions, date, and search type.

Instructions

Retrieve Google Search Console search analytics data.

Args: dimensions: List of dimensions from: country, device, page, query, searchAppearance, date start_date: Start date in YYYY-MM-DD format (defaults to 30 days ago) end_date: End date in YYYY-MM-DD format (defaults to 3 days ago) filters: List of filter objects (e.g., [{"dimension": "country", "operator": "equals", "expression": "usa"}]) search_type: Type of search ('web', 'image', 'video', 'news', 'discover', 'googleNews') row_limit: Maximum number of rows to return (max 25000) start_row: Starting row for pagination (0-based) summary_only: If True, returns only aggregated totals (Token Efficient)

Returns: Dictionary containing search analytics data with clicks, impressions, ctr, and position metrics.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
filtersNo
end_dateNo
row_limitNo
start_rowNo
dimensionsNo
start_dateNo
search_typeNoweb
summary_onlyNo
Behavior4/5

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

With no annotations, the description carries the full burden. It discloses many behavioral aspects: parameter defaults (e.g., start_date defaults to 30 days ago), pagination via start_row, and token efficiency for summary_only. However, it omits details on authentication, rate limits, or error handling, preventing a 5.

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 well-structured with an Args list and Returns section. It is slightly verbose due to parameter enumeration, but every sentence provides value. It is front-loaded with the main purpose and organized logically.

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

Completeness4/5

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

Given 8 parameters, no output schema, and no annotations, the description covers the essentials: return format, pagination, and filter examples. It could mention related tools (e.g., list_available_dimensions) for dimension values, but it remains complete enough for most scenarios.

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 coverage is 0%, but the description compensates fully by explaining each of the 8 parameters, including types, defaults, and an example for filters. This adds substantial meaning beyond the bare schema, making it highly useful for correct invocation.

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 'Retrieve Google Search Console search analytics data,' using a specific verb and resource. This distinguishes it from sibling tools like list_gsc_sites and get_sitemaps, which focus on site management or sitemaps.

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

Usage Guidelines3/5

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

The description does not explicitly state when to use this tool versus alternatives, nor does it mention any exclusions or when not to use it. It implies usage through parameter explanations but lacks direct guidance, so it earns a 3.

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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