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surendranb

Google Analytics MCP Server

by surendranb

get_ga4_data

Read-only

Retrieve GA4 analytics data with validated dimensions and metrics to prevent errors. Always use schema tools first to discover correct field names.

Instructions

Retrieve GA4 data with built-in intelligence for better and safer results.

Returns on success: {"data": [...], "metadata": {...}, "_skills_tip": "..."} Returns on volume warning: {"warning": "...", "estimated_rows": N, "suggestions": [...]} Returns on error: {"error": "..."}

CRITICAL WORKFLOW — follow this sequence every time:

  1. DISCOVER FIELDS: NEVER guess dimension or metric names. Call search_schema, list_dimension_categories, or list_metric_categories FIRST to verify exact API names for this property. Guessing costs you a failed round-trip.

  2. DISCOVER PATTERN: For any domain-specific analysis, call search_skills('<topic>') BEFORE querying to get the proven methodology — correct dimensions, metrics, filters, and how to interpret the result. One extra call prevents multiple failures. Use for: traffic diagnosis, attribution, ecommerce, channel acquisition, content performance, geo/device segmentation, AI referrals, bot detection.

  3. RETRIEVE: Call get_ga4_data with the verified fields and the skill's pattern.

  4. TROUBLESHOOT: On schema error, invalid field, or filter parse error — do NOT retry by guessing. Your training may predate current GA4 (UA was sunset 2023-07-01). Call search_schema('<keyword>') to find the current name in THIS property, or search_skills('ua-to-ga4' | 'common-metric-names' | 'filter-structures') for the mapping.

FIELD NAMES — GA4 API names vs common wrong guesses:

  • 'screenPageViews' not 'uniquePageviews' or 'pageViews'

  • 'totalUsers' not 'users'

  • 'keyEvents' not 'conversions' or 'goalCompletionsAll'

  • 'sessionKeyEventRate' not 'sessionConversionRate' or 'conversionRate' (GA4 renamed conversions→key events, 2024)

  • 'userEngagementDuration' not 'timeOnPage' or 'avgTimeOnPage'

  • 'averageSessionDuration' not 'avgSessionDuration'

  • 'itemsViewed' not 'itemViews'

  • 'ecommercePurchases' not 'purchases'

  • 'sessionDefaultChannelGroup' not 'sessionDefaultChannelGrouping'

  • 'sessionSource'/'sessionMedium' not 'source'/'medium'

  • All names are camelCase — never snake_case (page_path → pagePath, event_name → eventName)

  • 'bounceRate' and 'newUsers' are correct as-is

DATE RANGES:

  • Format: 'YYYY-MM-DD' or relative strings: '7daysAgo', '30daysAgo', 'yesterday', 'today'

  • 'NdaysAgo' counts back from today, excluding today. 'yesterday' = last complete day.

  • Period comparison (YoY, WoW): run two separate queries with different date ranges, then compare the results. The API does not support multi-period in one call.

SCOPE RULES — incompatible combinations return a 400 error:

  • Session dims (sessionSource, sessionMedium, sessionCampaignName) → use with sessions, bounceRate, sessionKeyEventRate. NOT with eventCount.

  • Event dims (eventName) → use with eventCount. NOT with sessions.

  • User dims (firstUserSource, firstUserMedium) → use with totalUsers, newUsers. NOT sessions.

  • Safe with any metric: date, deviceCategory, country, city, pagePath, pageTitle.

FILTER STRUCTURE:

  • Simple: {"filter": {"fieldName": "sessionSource", "stringFilter": {"value": "google", "matchType": "CONTAINS"}}}

  • AND: {"andGroup": {"expressions": [{"filter": {...}}, {"filter": {...}}]}}

  • OR: {"orGroup": {"expressions": [{"filter": {...}}, {"filter": {...}}]}}

  • NOT: {"notExpression": {"filter": {...}}}

  • Wrong keys that break filters: and_filter→andGroup, or_filter→orGroup, not_filter→notExpression, filters→expressions, field→fieldName

Args: dimensions: GA4 dimension names (verified via schema tools, e.g. ["date", "city"]). metrics: GA4 metric names (verified via schema tools, e.g. ["totalUsers", "sessions"]). date_range_start: Start date — 'YYYY-MM-DD' or '7daysAgo', '30daysAgo', 'yesterday'. date_range_end: End date — 'YYYY-MM-DD' or 'yesterday', 'today'. dimension_filter: Optional FilterExpression dict. camelCase and snake_case both accepted. limit: Max rows to return. Defaults to 1000. estimate_only: If True, returns only estimated row count without fetching data. proceed_with_large_dataset: Set True to bypass the 2500-row volume warning. enable_aggregation: If True, uses server-side aggregation when no date dimension. Default True. intent: Short plain-English description of what the user is trying to learn. E.g. "which channels drive most signups", "bot traffic audit for last month".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
intentNo
metricsNo
dimensionsNo
estimate_onlyNo
date_range_endNoyesterday
date_range_startNo7daysAgo
dimension_filterNo
enable_aggregationNo
proceed_with_large_datasetNo
Behavior5/5

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

Annotations only say readOnlyHint=true. Description adds volume warnings, estimate_only behavior, server-side aggregation, scope rules, filter structures, date range formats, and field name corrections. No contradiction with annotations.

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?

Very detailed and well-structured with clear sections (CRITICAL WORKFLOW, FIELD NAMES, etc.). Some redundancy (e.g., date range repeated in multiple formats), but overall efficient for the complexity. Could be slightly more concise.

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?

Given 10 parameters, no output schema, and complex GA4 domain, the description covers workflow, field mapping, date ranges, scope rules, filter structures, and parameter details. No gaps identified.

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%, but the description's 'Args' section provides complete semantics for all 10 parameters, including types, defaults, usage examples, and constraints. Fully compensates for missing schema descriptions.

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?

Description clearly states 'Retrieve GA4 data with built-in intelligence' and provides extensive specifics. Distinguishes from sibling tools like search_schema and search_skills by detailing the workflow where this tool is the final retrieval step after discovery.

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 workflow: discover fields via schema tools, discover pattern via search_skills, then retrieve. Includes troubleshooting steps and when not to guess. Clearly guides when to use this tool vs alternatives.

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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