Google Analytics MCP vs Google Search Console
A side-by-side look at the Google Analytics MCP and Google Search Console MCP servers: transport, auth, tools, quality signals, and the config to install either one in Claude, Cursor, Windsurf or VS Code.
G
Google Search ConsoleMarketing · Local subprocess (stdio)Google Search Console: 37 tools, every API endpoint, hourly data, history beyond 16 months.53GoodAuth: not declaredVerdict: choose Google Analytics MCP for marketing work as a local process; choose Google Search Console for marketing as a local process. You can also run both side by side in one mcpServers block.
Which MCP server should you choose?
Choose Google Analytics MCP when
- You need dedicated capabilities in the Marketing domain.
- You prefer a local stdio subprocess transport.
- You want tools like
check_for_updates, list_properties, search_schema.
GChoose Google Search Console when
- You need dedicated capabilities in the Marketing domain.
- You prefer a local stdio subprocess transport.
- You want tools like
performance_overview, hourly_performance, data_freshness.
Feature & specification comparison
Rows where the two servers differ are marked.
| Spec | G Google Search Console | |
|---|---|---|
| Category | Marketing | Marketing |
| Quality signal | 63/100 · Good | 53/100 · Good |
| Install command | Confirmed | Confirmed |
| Transport | Local subprocess (stdio) | Local subprocess (stdio) |
| Auth | Not declared | Not declared |
| Pricing | Not declared | Not declared |
| Required env vars | None listed | None listed |
| Verified owner | Community listing | Community listing |
| GitHub stars | 243 | 2 |
| Directory activity | 3 views 0 installs 0 upvotes | 1 views 0 installs 0 upvotes |
| Tools | 12 | 17 |
| Full listing | View Google Analytics MCP | View Google Search Console |
Tools & capabilities
Google Analytics MCP tools 12
check_for_updatesCheck PyPI for newer versions of this MCP server and get upgrade instructions.list_propertiesList all GA4 properties associated with an account.search_schemaSearch for a keyword across all dimensions and metrics for this property. Returns a ranked list of up to 10 matching fields scored by relevance. Returns: {"top_results": {"DIMENSION: api_name": score, "METRIC: api_name": score, ...}} Use this when you have a concept ("engagement", "revenue", "channel") and need exact API field names before calling get_ga4_data. Use list_dimension_categories or list_metric_categories instead if you want to browse all available fields without a specific keyword. Args: keyword: One or more keywords to search for (e.g., "user", "campaign revenue").get_property_schemaReturns the complete schema for the configured GA4 property, including all available dimensions and metrics (standard and custom). Warning: This can be a very large object (10k+ tokens). Use search_schema for most discovery tasks.list_dimension_categoriesList all dimension categories for this GA4 property, with a count of dimensions in each category. Returns: {"dimension_categories": {"Category Name": count, ...}} Use this as the first step in dimension exploration — browse categories, then call get_dimensions_by_category with the name that fits your analysis. Use search_schema instead if you already have a keyword to search for.list_metric_categoriesList all metric categories for this GA4 property, with a count of metrics in each category. Returns: {"metric_categories": {"Category Name": count, ...}} Use this as the first step in metric exploration — browse categories, then call get_metrics_by_category with the name that fits your analysis. Use search_schema instead if you already have a keyword to search for.get_dimensions_by_categoryReturn all dimensions in a specific category with their API names and descriptions. Returns: {"dimension_api_name": "description", ...} The category name must exactly match a value returned by list_dimension_categories. Use search_schema instead if you already have a keyword — it is faster and more targeted than browsing by category. Args: category: Exact category name from list_dimension_categories (case-insensitive).get_metrics_by_categoryReturn all metrics in a specific category with their API names and descriptions. Returns: {"metric_api_name": "description", ...} The category name must exactly match a value returned by list_metric_categories. Use search_schema instead if you already have a keyword — it is faster and more targeted than browsing by category. Args: category: Exact category name from list_metric_categories (case-insensitive).get_ga4_dataRetrieve GA4 data with built-in intelligence for better and safer results. Returns on success: {"data": [...], "metadata": {...}, "_skills_tip": "..."}. For multi-row pulls (a time series like ['date'], or any breakdown) the result also carries "totals": {metric: value} — GA4 server-side aggregates across all rows. Read the period figure from "totals"; do NOT sum the rows yourself. (Additive metrics sum; rate metrics are period-computed by GA4.) 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, asks GA4 for server-side metric totals whenever a dimension splits the data across rows (e.g. a 7-day ['date'] pull), returned in a "totals" block so the model needn't sum rows. 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".get_troubleshooting_guideReturns the troubleshooting/setup guide for a topic, served from inside the server (no network needed). Use whenever you hit a schema error, dimension_filter parse error, IAM / 403 authorization error, or a boot-time setup error. Args: topic: One of "setup", "iam", or "schema".search_skillsFetch analytical recipes and how-to guides from the GA4 skills library. Skills are domain-specific playbooks for common GA4 analysis patterns — exact dimensions, metrics, filters, and interpretation logic for each use case. Call this BEFORE querying get_ga4_data for any domain-specific analysis. Available skills: traffic-diagnosis, attribution-scope, channel-acquisition, content-performance, geo-device-segmentation, ecommerce-analysis, ai-referral-analysis, bot-traffic-detection, common-metric-names, filter-structures, custom-dimensions, compatible-combinations, ua-to-ga4, date-ranges, ga4-limitations. Usage: - search_skills("") → returns full index of all skills - search_skills("ecommerce") → returns the ecommerce-analysis skill - search_skills("ua-to-ga4") → returns the UA→GA4 field name mapping Args: query: A skill name (exact slug) or empty string to browse the full index.setup_ga4_accessInteractively fix a broken GA4 MCP setup (missing property ID, missing or expired credentials, or missing GA4 access) by asking the user for the needed input through the client, then re-initializing without a restart. Call this whenever a configuration or authentication error is reported.
Google Search Console tools 17
performance_overview*"How did example.com do in the last 6 months, month by month?"*hourly_performance*"Show the last 24 hours"* · *"Show last week as Tehran days"*data_freshness*"Is yesterday's data complete yet?"*build_query_regex*"Give me a regex for خرید خودرو that matches every spelling"*sync_history*"Sync the last 16 months of example.com"*history_status*"What history do I have saved?"*history_sql*"Run this SQL on my history: …"*inspect_url*"Is /new-article/ indexed? What canonical did Google pick?"*inspect_urls*"Inspect these 30 URLs"*indexing_summary*"Which of these pages have indexing issues?"*inspection_quota*"How many inspections do I have left today?"*list_sitemaps*"List my sitemaps and their errors"*- +5 more on the listing page
Ready-to-paste client configs
Paste either block (or both) into your client config, e.g. claude_desktop_config.json or ~/.cursor/mcp.json. We only show a config when the install command is confirmed.
Google Analytics MCP
{
"mcpServers": {
"google-analytics-mcp": {
"command": "uvx",
"args": [
"google-analytics-mcp"
]
}
}
}
Google Search Console
{
"mcpServers": {
"google-search-console-8": {
"command": "uvx",
"args": [
"gsc-mcp-full"
]
}
}
}
Frequently asked questions
Google Analytics MCP is categorized under Marketing and uses a local stdio subprocess. Google Search Console belongs to Marketing and uses a local stdio subprocess. Pick Google Analytics MCP for marketing work and Google Search Console for marketing.
Copy the mcpServers block from the config section on this page into your client's config (for example claude_desktop_config.json or ~/.cursor/mcp.json), then fully restart the client.
Google Analytics MCP has no declared pricing — check the project's own site. Google Search Console has no declared pricing — check the project's own site. If a server needs environment variables such as API keys, define them under the env key in your client's MCP config.
Yes. MCP clients support multiple servers: add google-analytics-mcp and google-search-console-8 as separate keys in the same mcpServers object, and your assistant will call tools from either one.
On AllMCPs, Google Analytics MCP has a quality signal of 63/100 (Good) with 3 views, 0 installs and 243 GitHub stars. Google Search Console has 53/100 (Good) with 1 views, 0 installs and 2 GitHub stars.