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io.github.surendranb由 GitHub 帳號 surendranb 發布v2.11.7更新於 Nov 3, 2026
GA4 MCP server for AI agents: schema discovery, server-side aggregation, analysis-ready defaults.
本機 MCP 伺服器,讓助理探索 GA4 資源的欄位結構,並以正確的維度與指標名稱執行 Google Analytics 4 報表。
Desktop only,透過 STDIO。 STDIO 服務會啟動本機處理程序,因此需要 SourceWeft 桌面主機。
參照 儲存庫 中的啟動說明。
Model Context Protocol (MCP) server for Google Analytics 4: real-time query exploration, schema discovery, metric aggregation, and audience insights for AI agents.
[CI] [PyPI version] [npm version] [OpenSSF Scorecard] [License: MIT]
🌐 Live Documentation & Web Portal: https://ga4mcp.com/
mcp.json)claude_desktop_config.json)This server ships with built-in analytical recipes that load dynamically from GitHub:
traffic-diagnosis: Step-by-step root cause analysis for sudden traffic drops.channel-acquisition: Best-practice channel grouping and attribution modeling.ecommerce-analysis: Revenue, item purchase rate, and conversion funnel analysis.ai-referral-analysis: Tracks and isolates referral traffic from ChatGPT, Claude, Perplexity, and Gemini.This package collects anonymous, non-PII diagnostic telemetry (command executions, latency, error codes) to improve tool reliability. No queries, user credentials, personal data, source code, or environment variables are ever collected or stored.
You can opt out anytime by setting either of the following environment variables:
MIT License. See LICENSE for details.
來源:README.md,提交 d8afdf5
Check PyPI for newer versions of this MCP server and get upgrade instructions.
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List all GA4 properties associated with an account.
account_idSearch 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").
keyword必填Returns 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.
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List 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.
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List 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.
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Return 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).
category必填Return 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).
category必填Retrieve 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'
limitintentmetricsdimensionsestimate_onlydate_range_enddate_range_startdimension_filterenable_aggregationproceed_with_large_datasetReturns 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".
topic必填Fetch 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: gcp-setup, 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("gcp-setup") → returns the GCP service-account setup playbook - 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.
query必填Interactively 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.
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已滿足適用檢查項的 90%
該評分衡量此收錄條目的完整程度,依據註冊中心條目、端點與公開來源計算得出。它不是對伺服器本身的評價,也不表示伺服器安全或目前可用:可連通性是帶日期的檢查結果,評分也不包含安裝量或使用者評價。
請在註冊中心條目中宣告憑證請求標頭,或保持端點能夠回應探測,以便判定其認證方式。
# 1-Line Universal Installer (Auto-configures Claude Desktop, Cursor, Claude Code, Antigravity, VS Code, Zed, Windsurf)curl -fsSL "https://ga4.builditwithai.xyz/install" | bash
# Or run directly via your preferred runtime:uvx google-analytics-mcpuvx --from google-analytics-mcp ga4-mcp-serverpython -m ga4_mcpnpx -y @surendranb/google-analytics-mcpclaude mcp add google-analytics -- uvx google-analytics-mcp{ "mcpServers": { "google-analytics": { "command": "uvx", "args": ["google-analytics-mcp"] } }}{ "mcpServers": { "google-analytics": { "command": "uvx", "args": ["google-analytics-mcp"], "env": { "GA4_PROPERTY_ID": "your_ga4_property_id", "GOOGLE_APPLICATION_CREDENTIALS": "/path/to/service_account.json" } } }}{ "mcpServers": { "google-analytics": { "command": "npx", "args": ["-y", "@surendranb/google-analytics-mcp"] } }}export DO_NOT_TRACK=1# orexport MCP_TELEMETRY_OPT_OUT=1