Andrew Yim
214247ace2
Create workspace for building Google Analytics Claude Skill with: - 5 independent components (MCP setup, skill, dimension explorer, Slack reporter, realtime watcher) - Comprehensive project plan and documentation - Step-by-step setup guides for each component Components: 1. MCP Setup - GA4 + BigQuery MCP server installation 2. GA Agent Skill - Core Claude Skill for interactive analysis 3. Dimension Explorer - Validate dims/metrics with explanations 4. Slack Reporter - Automated reports to Slack (P2) 5. Realtime Watcher - Real-time monitoring (deferred) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
155 lines
3.1 KiB
Markdown
155 lines
3.1 KiB
Markdown
# Component 3: Dimension Explorer
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**Type:** Utility (MCP Server / CLI / Reference)
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**Priority:** P1
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**Status:** Not Started
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## Goal
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Validate GA4 dimensions and metrics with detailed explanations.
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## Features
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- List all available dimensions/metrics
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- Validate if a dimension/metric exists
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- Get description, data type, category
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- Fuzzy search for typos
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- Compatibility checking
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## Implementation Options
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| Option | Approach | Effort |
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|--------|----------|--------|
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| A | Reference JSON in skill | Low |
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| B | CLI tool | Low |
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| C | MCP Server | Medium |
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**Recommendation:** Start with A, upgrade to C later.
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## Structure
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```
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03-dimension-explorer/
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├── README.md
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├── fetch_metadata.py # Fetch from GA4 Admin API
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├── data/
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│ ├── dimensions.json # All dimensions
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│ └── metrics.json # All metrics
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├── explorer.py # CLI tool (optional)
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└── requirements.txt
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```
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## Data Format
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### dimensions.json
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```json
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{
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"dimensions": [
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{
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"apiName": "sessionSource",
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"uiName": "Session source",
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"description": "The source that initiated a session",
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"category": "Traffic source",
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"deprecatedApiNames": []
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}
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]
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}
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```
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### metrics.json
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```json
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{
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"metrics": [
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{
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"apiName": "activeUsers",
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"uiName": "Active users",
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"description": "Number of distinct users who visited",
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"category": "User",
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"type": "TYPE_INTEGER",
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"expression": ""
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}
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]
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}
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```
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## fetch_metadata.py
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```python
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from google.analytics.admin import AnalyticsAdminServiceClient
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def fetch_metadata(property_id: str):
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"""Fetch all dimensions and metrics for a property."""
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client = AnalyticsAdminServiceClient()
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# Get metadata
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metadata = client.get_metadata(
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name=f"properties/{property_id}/metadata"
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)
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dimensions = [
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{
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"apiName": d.api_name,
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"uiName": d.ui_name,
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"description": d.description,
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"category": d.category,
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}
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for d in metadata.dimensions
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]
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metrics = [
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{
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"apiName": m.api_name,
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"uiName": m.ui_name,
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"description": m.description,
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"category": m.category,
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"type": m.type_.name,
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}
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for m in metadata.metrics
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]
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return {"dimensions": dimensions, "metrics": metrics}
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```
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## Usage
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### As Reference (Option A)
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Include `data/dimensions.json` and `data/metrics.json` in the GA Agent skill's `references/` folder.
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### As CLI (Option B)
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```bash
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# Validate a dimension
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python explorer.py validate --dimension sessionSource
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# Search for metrics
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python explorer.py search --query "user"
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# List by category
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python explorer.py list --category "Traffic source"
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```
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### As MCP Server (Option C)
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```bash
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# Run server
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python server.py
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# Claude can use tools like:
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# - validate_dimension
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# - validate_metric
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# - search_metadata
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# - list_by_category
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```
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## Checklist
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- [ ] fetch_metadata.py created
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- [ ] Metadata fetched and saved
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- [ ] dimensions.json generated
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- [ ] metrics.json generated
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- [ ] explorer.py (optional)
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- [ ] Integrated with GA Agent skill
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