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MCP server · Commerce

Engageable Analytics

Unified analytics MCP server for GA4, Mixpanel, and PostHog.

Unclaimed commerce
37Low

Scored 6 months ago · breakdown

About Engageable Analytics

Engageable Analytics is an MCP server in the Commerce category: unified analytics MCP server for GA4, Mixpanel, and PostHog.

Install

uvx
uvx engageable
pip
pip install engageable

This server has no ConduID identity, so agent calls to it are not receipted. Pin the version you install and review the source before granting it credentials.

Tools (9)

analyze_cohortDefine and compare user cohorts

Parameters not published.

analyze_funnelMulti-step conversion funnel with drop-off rates

Parameters not published.

analyze_retentionCohort retention curves (D1/D7/D30)

Parameters not published.

analyze_trendsTime-series analysis with trend detection, change points, anomalies

Parameters not published.

askNatural language analytics questions (routes to other tools via LLM)

Parameters not published.

compare_segmentsA/B tests, before/after, segment breakdown with statistical significance

Parameters not published.

configure_sourceConnect a new data source (saves to `~/.engageable/credentials.json`)

Parameters not published.

detect_anomaliesFind spikes, drops, and unusual patterns

Parameters not published.

get_sourcesList connected data sources

Parameters not published.

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Security checks

  • ✓README presentThe repository ships documentation.

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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

README copy captured when this server was indexed; it may lag the repository. The original is authoritative.

Questions

About Engageable Analytics

How do I install Engageable Analytics?

Run uvx engageable, then add the server to your MCP client's configuration.

Is Engageable Analytics safe to use with an AI agent?

Its trust score is 37 out of 100 (low). It passes 1 of the 1 static security checks run so far. It has no ConduID identity yet, so agent calls to it are not receipted.

Is Engageable Analytics still maintained?

Conduid hasn't recorded a commit date for this repository yet. Check the repository directly for recent activity.