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surendranb

Google Analytics MCP Server

by surendranb

get_ga4_data

Read-only

Query Google Analytics 4 data with validated dimension and metric names, filters, date ranges, and aggregation, including volume warnings for large datasets.

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?

Despite readOnlyHint annotation, the description adds extensive behavioral context: return structures (success, warning, error), field name corrections, date range formats, scope rules, filter structure, and parameter explanations. 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?

The description is lengthy but well-structured with clear sections, bullet points, and hierarchical organization. Every sentence adds value; however, a 4 is given because it could be slightly more concise without losing essential information.

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 the tool's complexity (10 parameters, no output schema, nested objects), the description is exceptionally complete. It covers workflow, field mappings, date handling, scope rules, filter syntax, troubleshooting, and parameter semantics, leaving no critical gaps.

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 carries full burden. It explains each parameter in detail: dimensions, metrics, date ranges, dimension_filter, limit, estimate_only, proceed_with_large_dataset, enable_aggregation, and intent. Adds substantial meaning beyond the empty schema.

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 the tool retrieves GA4 data with built-in intelligence, and it distinguishes itself from sibling tools by emphasizing that it is for actual retrieval after schema discovery. The verb 'Retrieve' plus the resource 'GA4 data' is specific and actionable.

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?

The description provides an explicit 4-step workflow, warns against guessing field names, directs to alternative tools like search_schema and search_skills for discovery, and includes troubleshooting guidance. It clearly states when to use this tool versus 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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