Local MCP server for Google Analytics APIs.
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GitHub stars
This repository contains the source code for running a local MCP server that interacts with APIs for Google Analytics. It utilizes the Google Analytics Admin API and Google Analytics Data API to provide various tools for use with LLMs, enabling users to retrieve account information, run reports, and more.
Follow the setup instructions to configure Python, credentials, and Gemini.
Add new capabilities to Claude beyond text generation
Example
Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Automate multi-step workflows combining AI and external tools
Example
Research → Summarize → Create document → Send notification
Complete complex tasks end-to-end without manual steps
Share your MCP server with the developer community
We wired Google Analytics MCP Server (Experimental) into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
We evaluated Google Analytics MCP Server (Experimental) against two servers with overlapping tools; this profile had the clearer scope statement.
Useful MCP listing: Google Analytics MCP Server (Experimental) is the kind of server we cite when onboarding engineers to host + tool permissions.
We evaluated Google Analytics MCP Server (Experimental) against two servers with overlapping tools; this profile had the clearer scope statement.
Google Analytics MCP Server (Experimental) is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
Useful MCP listing: Google Analytics MCP Server (Experimental) is the kind of server we cite when onboarding engineers to host + tool permissions.
Strong directory entry: Google Analytics MCP Server (Experimental) surfaces stars and publisher context so we could sanity-check maintenance before adopting.
I recommend Google Analytics MCP Server (Experimental) for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
Google Analytics MCP Server (Experimental) has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
Google Analytics MCP Server (Experimental) is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
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Google Analytics MCP Server (Experimental) PyPI version Python 3.10+ GitHub branch check runs PyPI - Downloads GitHub stars GitHub forks YouTube Video Views
This repo contains the source code for running a local MCP server that interacts with APIs for Google Analytics.
Join the discussion and ask questions in the 🤖-analytics-mcp channel on Discord.
Tools 🛠️ The server uses the Google Analytics Admin API and Google Analytics Data API to provide several Tools for use with LLMs.
Retrieve account and property information 🟠 get_account_summaries: Retrieves information about the user's Google Analytics accounts and properties. get_property_details: Returns details about a property. list_google_ads_links: Returns a list of links to Google Ads accounts for a property. Run core reports 📙 run_report: Runs a Google Analytics report using the Data API. run_funnel_report: Runs a Google Analytics funnel report using the Data API. get_custom_dimensions_and_metrics: Retrieves the custom dimensions and metrics for a specific property. Run realtime reports ⏳ run_realtime_report: Runs a Google Analytics realtime report using the Data API. Setup instructions 🔧 ✨ Watch the Google Analytics MCP Setup Tutorial on YouTube for a step-by-step walkthrough of these instructions.
Watch the video
Setup involves the following steps:
Configure Python. Configure credentials for Google Analytics. Configure Gemini. Configure Python 🐍 Install pipx.
Enable APIs in your project ✅ Follow the instructions to enable the following APIs in your Google Cloud project:
Google Analytics Admin API Google Analytics Data API Configure credentials 🔑 Configure your Application Default Credentials (ADC). Make sure the credentials are for a user with access to your Google Analytics accounts or properties.
Credentials must include the Google Analytics read-only scope:
https://www.googleapis.com/auth/analytics.readonly Check out Manage OAuth Clients for how to create an OAuth client.
Here are some sample gcloud commands you might find useful:
Set up ADC using user credentials and an OAuth desktop or web client after downloading the client JSON to YOUR_CLIENT_JSON_FILE.
gcloud auth application-default login
--scopes https://www.googleapis.com/auth/analytics.readonly,https://www.googleapis.com/auth/cloud-platform
--client-id-file=YOUR_CLIENT_JSON_FILE
Set up ADC using service account impersonation.
gcloud auth application-default login
--impersonate-service-account=SERVICE_ACCOUNT_EMAIL
--scopes=https://www.googleapis.com/auth/analytics.readonly,https://www.googleapis.com/auth/cloud-platform
When the gcloud auth application-default command completes, copy the PATH_TO_CREDENTIALS_JSON file location printed to the console in the following message. You'll need this for the next step!
Credentials saved to file: [PATH_TO_CREDENTIALS_JSON] Configure Gemini Install Gemini CLI or Gemini Code Assist.
Create or edit the file at ~/.gemini/settings.json, adding your server to the mcpServers list.
Replace PATH_TO_CREDENTIALS_JSON with the path you copied in the previous step.
We also recommend that you add a GOOGLE_CLOUD_PROJECT attribute to the env object. Replace YOUR_PROJECT_ID in the following example with the project ID of your Google Cloud project.
{ "mcpServers": { "analytics-mcp": { "command": "pipx", "args": ["run", "analytics-mcp"], "env": { "GOOGLE_APPLICATION_CREDENTIALS": "PATH_TO_CREDENTIALS_JSON", "GOOGLE_PROJECT_ID": "YOUR_PROJECT_ID" } } } } Try it out 🥼 Launch Gemini Code Assist or Gemini CLI and type /mcp. You should see analytics-mcp listed in the results.
Here are some sample prompts to get you started:
Ask what the server can do:
what can the analytics-mcp server do? Ask about a Google Analytics property
Give me details about my Google Analytics property with 'xyz' in the name Prompt for analysis:
what are the most popular events in my Google Analytics property in the last 180 days? Ask about signed-in users:
were most of my users in the last 6 months logged in? Ask about property configuration:
what are the custom dimensions and custom metrics in my property? Contributing ✨ Contributions welcome! See the Contributing Guide.
Prerequisites
Time Estimate
15-60 minutes depending on server complexity
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
Compatibility
✓ Use when
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid when
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.
