
Google Tasks MCP Server
The Google Tasks MCP Server bridges AI assistants with Google Tasks, enabling seamless management and automation of tasks directly through standardized protocol...
Seamlessly bridge Google Analytics 4 with AI-powered developer workflows and assistants using the Google Analytics MCP Server for natural language analytics, automated reporting, and actionable insights.
The Google Analytics MCP Server enables seamless integration of Google Analytics 4 (GA4) data with AI assistants and development tools like Claude, Cursor, and Windsurf using the Model Context Protocol (MCP). By acting as a bridge between MCP clients and the GA4 API, it allows users to query website traffic, user behavior, and analytics data in natural language, unlocking access to over 200 dimensions and metrics. This empowers AI agents to automate reporting, perform in-depth data analysis, and provide actionable insights directly inside developer workflows or AI-powered tools, streamlining the process of making data-informed decisions without manual dashboard navigation.
No specific prompt templates are mentioned in the repository.
No explicit resources are listed in the repository.
ga4_mcp_server.py
) is not detailed in the available files.mcpServers
configuration:{
"mcpServers": {
"google-analytics-mcp": {
"command": "python3",
"args": ["-m", "google_analytics_mcp"]
}
}
}
claude-config-template.json
as a starting point.mcpServers
field in your Claude configuration:{
"mcpServers": {
"google-analytics-mcp": {
"command": "python3",
"args": ["-m", "google_analytics_mcp"]
}
}
}
{
"mcpServers": {
"google-analytics-mcp": {
"command": "python3",
"args": ["-m", "google_analytics_mcp"]
}
}
}
{
"mcpServers": {
"google-analytics-mcp": {
"command": "python3",
"args": ["-m", "google_analytics_mcp"]
}
}
}
Securing API Keys (using environment variables):
To provide sensitive credentials (like Google Analytics API keys or service account files), use environment variables for security. Example configuration:
{
"mcpServers": {
"google-analytics-mcp": {
"command": "python3",
"args": ["-m", "google_analytics_mcp"],
"env": {
"GOOGLE_APPLICATION_CREDENTIALS": "/path/to/your/credentials.json"
},
"inputs": {
"property_id": "YOUR_GA4_PROPERTY_ID"
}
}
}
}
Using MCP in FlowHunt
To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:
Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:
{
"google-analytics-mcp": {
"transport": "streamable_http",
"url": "https://yourmcpserver.example/pathtothemcp/url"
}
}
Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “google-analytics-mcp” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.
Section | Availability | Details/Notes |
---|---|---|
Overview | ✅ | |
List of Prompts | ⛔ | No prompt templates found |
List of Resources | ⛔ | Not explicitly listed |
List of Tools | ⛔ | Not explicitly listed |
Securing API Keys | ✅ | Env variable usage shown in config example |
Sampling Support (less important in evaluation) | ⛔ | Not documented |
Between the documentation and the code, Google Analytics MCP provides a clear overview and setup instructions, but lacks detailed documentation on prompts, resources, and tools. For security, it supports environment variable configuration. Roots and sampling are not referenced.
Based on the tables above, this MCP server scores well for overview and setup, but is missing detail on prompts, tools, and resources. It is best for users already familiar with GA4 and MCP concepts who do not need extensive prompt/workflow templates.
Has a LICENSE | ✅ (MIT) |
---|---|
Has at least one tool | ⛔ |
Number of Forks | 9 |
Number of Stars | 57 |
It's a bridge between Google Analytics 4 (GA4) and AI/developer tools via the Model Context Protocol (MCP), enabling natural language access to analytics data, automated reporting, and seamless workflow integration.
Natural language analytics queries, automated GA4 reporting, workflow integration in tools like Cursor or Windsurf, AI-driven insights, and cross-source data analysis with other MCP servers.
Store sensitive information such as API keys or service account files in environment variables. For example, set 'GOOGLE_APPLICATION_CREDENTIALS' to your credentials file path in the MCP server config.
It’s best suited for users already familiar with GA4 and MCP, as detailed prompt and resource templates are not provided.
No explicit prompt templates or detailed tool documentation are included. The server focuses on connectivity and data access.
Add the MCP component to your FlowHunt flow, open its configuration, and insert the MCP server details in JSON format. Once configured, your AI agent will have access to Google Analytics data for enhanced analytics capabilities.
Unlock powerful GA4 analytics in your AI workflows, automate reporting, and empower your team to make data-driven decisions directly from your favorite tools.
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