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Unified analytics MCP server for GA4, Mixpanel, and PostHog.
About
Unified analytics MCP server for GA4, Mixpanel, and PostHog.
Security Report
Valid MCP server (2 strong, 2 medium validity signals). 3 known CVEs in dependencies (0 critical, 3 high severity) ⚠️ Package registry links to a different repository than scanned source. Imported from the Official MCP Registry.
9 files analyzed · 4 issues found
Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.
Permissions Required
This plugin requests these system permissions. Most are normal for its category.
What You'll Need
Set these up before or after installing:
Environment variable: ANTHROPIC_API_KEY
Environment variable: POSTHOG_API_KEY
Environment variable: POSTHOG_PROJECT_ID
Environment variable: GA4_CREDENTIALS_JSON
Environment variable: GA4_PROPERTY_ID
Environment variable: MIXPANEL_SERVICE_ACCOUNT_USERNAME
Environment variable: MIXPANEL_SERVICE_ACCOUNT_SECRET
Environment variable: MIXPANEL_PROJECT_ID
How to Install
Add this to your MCP configuration file:
{
"mcpServers": {
"tech-engageable-analytics": {
"env": {
"GA4_PROPERTY_ID": "your-ga4-property-id-here",
"POSTHOG_API_KEY": "your-posthog-api-key-here",
"ANTHROPIC_API_KEY": "your-anthropic-api-key-here",
"POSTHOG_PROJECT_ID": "your-posthog-project-id-here",
"MIXPANEL_PROJECT_ID": "your-mixpanel-project-id-here",
"GA4_CREDENTIALS_JSON": "your-ga4-credentials-json-here",
"MIXPANEL_SERVICE_ACCOUNT_SECRET": "your-mixpanel-service-account-secret-here",
"MIXPANEL_SERVICE_ACCOUNT_USERNAME": "your-mixpanel-service-account-username-here"
},
"args": [
"engageable"
],
"command": "uvx"
}
}
}Documentation
View on GitHubFrom the project's GitHub README.
Engageable
The open source analytics engine for AI agents.
One MCP server that connects to GA4, Mixpanel, PostHog, and more. 9 tools that replace per-platform integrations. Bring your own Anthropic key.
Quickstart
pip install engageable
export ANTHROPIC_API_KEY=sk-ant-...
export POSTHOG_API_KEY=phc_...
export POSTHOG_PROJECT_ID=12345
engageable-mcp
That's it. The MCP server is running on stdio. Connect it to Claude Desktop, Cursor, or any MCP client.
Claude Desktop
Add to ~/Library/Application Support/Claude/claude_desktop_config.json:
{
"mcpServers": {
"engageable": {
"command": "engageable-mcp",
"args": []
}
}
}
Restart Claude Desktop. You'll see 9 analytics tools available.
Docker
ANTHROPIC_API_KEY=sk-ant-... docker compose -f docker-compose.mcp.yml up
Connects via SSE at http://localhost:8080/sse.
Tools
| Tool | What it does |
|---|---|
get_sources | List connected data sources |
configure_source | Connect a new data source (saves to ~/.engageable/credentials.json) |
analyze_trends | Time-series analysis with trend detection, change points, anomalies |
compare_segments | A/B tests, before/after, segment breakdown with statistical significance |
detect_anomalies | Find spikes, drops, and unusual patterns |
analyze_retention | Cohort retention curves (D1/D7/D30) |
analyze_funnel | Multi-step conversion funnel with drop-off rates |
analyze_cohort | Define and compare user cohorts |
ask | Natural language analytics questions (routes to other tools via LLM) |
Supported Data Sources
| Source | Auth | What you need |
|---|---|---|
| PostHog | API key | POSTHOG_API_KEY + POSTHOG_PROJECT_ID |
| Mixpanel | Service account | MIXPANEL_SERVICE_ACCOUNT_USERNAME + MIXPANEL_SERVICE_ACCOUNT_SECRET + MIXPANEL_PROJECT_ID |
| Google Analytics 4 | Service account | GA4_CREDENTIALS_JSON (path or inline) + GA4_PROPERTY_ID |
Set these as environment variables, or use the configure_source tool to save them interactively to ~/.engageable/credentials.json. See credentials.example.json for the file format.
How It Works
Engageable exposes analytics tools via the Model Context Protocol (MCP). Any MCP-compatible client (Claude, Cursor, VS Code, custom agents) can discover and call these tools.
Each tool is a self-contained pipeline: parse the request, fetch data from the right connector, run analysis, return results. The ask tool adds an LLM routing layer for natural language questions.
Responses use CSV for tabular data (50% fewer tokens than JSON) with a 1000-cell budget to keep context windows manageable.
Architecture
MCP Client (Claude, Cursor, etc.)
│
▼
┌─────────────────────────────────────┐
│ MCP Server (stdio or SSE) │
│ - Dynamic tool registration │
│ - Credential injection │
│ - CSV response formatting │
├─────────────────────────────────────┤
│ Composite Skills (agent-facing) │
│ analyze_trends, compare_segments, │
│ detect_anomalies, analyze_funnel, │
│ analyze_retention, analyze_cohort, │
│ ask, get_sources, configure_source │
├─────────────────────────────────────┤
│ Connector Skills (internal) │ Analysis Skills (internal)
│ ga4_query, posthog_query, │ trend_detection, significance,
│ mixpanel_query, + metadata/probe │ cohort_retention, forecasting,
│ │ bayesian_ab, correlation, ...
└─────────────────────────────────────┘
License
MIT
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