Amplitude Navigator

Amplitude Navigator

This skill provides efficient navigation of the Amplitude MCP tools, reducing context overhead by documenting common query patterns, event taxonomies, and RDC-specific configurations.

RDC Context

Organization

• Organization ID: 228133
• Organization URL: realtor
• Organization Name: Realtor.com

Key Projects

Default Project: 558383 (Realtor - Production)

Tool Selection Matrix

Workflow Pattern

1. Search first - Use search to discover existing charts/events before building custom queries
2. Get properties - Use get_event_properties after finding exact event names
3. Query or build - Use existing charts (query_charts) or build custom (query_dataset)
4. Always reference - Include chart/metric links in responses for validation

Query Types Reference

Events Segmentation (Trends)

For time-series analysis of events.

{
  "name": "Daily Active Users",
  "type": "eventsSegmentation",
  "app": "558383",
  "params": {
    "range": "Last 30 Days",
    "events": [{"event_type": "_active", "filters": [], "group_by": []}],
    "metric": "uniques",
    "countGroup": "User",
    "groupBy": [],
    "interval": 1,
    "segments": [{"conditions": []}]
  }
}

Metric Options:

• uniques - Unique users
• totals - Event count
• average - Per user average
• pct_dau - % of DAU
• frequency - Distribution
• sums - Property sum (requires property in group_by)
• value_avg - Property average

Funnels (Conversion Analysis)

For step-by-step conversion analysis.

{
  "name": "Lead Funnel",
  "type": "funnels",
  "app": "558383",
  "params": {
    "range": "Last 30 Days",
    "events": [
      {"event_type": "pageview", "filters": [], "group_by": []},
      {"event_type": "cobrokelead", "filters": [], "group_by": []}
    ],
    "countGroup": "User",
    "segments": [{"conditions": []}]
  }
}

Key Parameters:

• conversionWindow: {"value": 1, "unit": "day"} - Time limit for completion
• order: "this_order" (default), "any_order", "exact_order"

Retention

For cohort retention analysis.

{
  "name": "New User Retention",
  "type": "retention",
  "app": "558383",
  "params": {
    "range": "Last 90 Days",
    "startEvent": {"event_type": "_new", "filters": [], "group_by": []},
    "retentionEvents": [{"event_type": "_active", "filters": [], "group_by": []}],
    "retentionMethod": "nday",
    "countGroup": "User",
    "interval": 1,
    "segments": [{"conditions": []}]
  }
}

Retention Methods:

• nday - Return on specific day
• rolling - Return on or after day
• bracket - Custom ranges via retentionBrackets

Sessions

For session-based metrics.

{
  "name": "Average Session Length",
  "type": "sessions",
  "app": "558383",
  "params": {
    "range": "Last 30 Days",
    "sessions": [{"filters": [], "group_by": []}],
    "countGroup": "User",
    "sessionType": "average",
    "segments": [{"conditions": []}]
  }
}

Session Types: average, totalSessions, peruser, averageTimePerUser, totalTime, length

Meta Event Types

Special system events available in all queries:

Property Syntax

Property Sources

Common Amplitude Core Properties

User/Session: user_id, amplitude_id, device_id, session_id Geographic: country, city, region, dma Device: platform, device, device_type, os, language Application: version, start_version, library

Filter Syntax

Segment-level filter (applies to all events):

"segments": [{
  "conditions": [{
    "type": "property",
    "group_type": "User",
    "prop_type": "user",
    "prop": "country",
    "op": "is",
    "values": ["United States"]
  }]
}]

Event-level filter (applies to specific event):

"events": [{
  "event_type": "pageview",
  "filters": [{
    "group_type": "User",
    "subprop_key": "page_type",
    "subprop_op": "is",
    "subprop_type": "event",
    "subprop_value": ["ldp"]
  }],
  "group_by": []
}]

Filter Operators: is, is not, contains, does not contain, set is, set is not, greater than, less than

Group By Syntax

Event property:

"group_by": [{"type": "event", "value": "platform"}]

User property:

"group_by": [{"type": "user", "value": "country"}]

Top-level groupBy (across all events):

"groupBy": [{"type": "user", "value": "country", "group_type": "User"}]

Time Parameters

Range Strings

• "Last N Days" - Last 7 Days, Last 30 Days, Last 90 Days
• "Last N Weeks" - Last 4 Weeks
• "Last N Months" - Last 3 Months
• "This Week", "This Month", "This Quarter", "This Year"

Unix Timestamps

For precise boundaries:

"start": 1727740800,
"end": 1730419199

ISO Dates

"start": "2025-01-01",
"end": "2025-01-31"

Interval Values

Important: Only these exact values are valid. Arbitrary intervals (2, 3, 14) will error.

Common RDC Events

Lead Events

Engagement Events

User Events

Experiment Events

Experiment Analysis

Querying Experiments

1. Search for experiment:

Amplitude:search(entityTypes=["EXPERIMENT"], query="feature_name")

2. Get experiment details:

Amplitude:get_experiments(ids=["123456"])

3. Query results (primary metric only by default):

Amplitude:query_experiment(id="123456")

4. With segment breakdown:

Amplitude:query_experiment(
  id="123456",
  groupBy=[{"type": "user", "value": "platform", "group_type": "User"}]
)

Interpreting Results

Key Fields:

• pValue - Statistical significance (< 0.05 = significant)
• relativeLift - Percentage change vs control
• absoluteLift - Raw metric difference
• ssrmTimeseries.srmDetected - Sample Ratio Mismatch flag
• recommendation - "rollout", "rollback", or null

Decision Framework:

• Significant + Positive Lift + No SRM → Rollout
• SRM Detected → Rollback (data quality issue)
• >30 days + p=1.0 → Consider terminating
• <14 days → Continue running

Session Replay

Basic Search

Amplitude:get_session_replays(
  projectId="558383",
  segmentFilters=[{"conditions": []}],
  limit=10
)

By Experiment Variant

Amplitude:get_session_replays(
  projectId="558383",
  segmentFilters=[{
    "conditions": [{
      "type": "property",
      "group_type": "User",
      "prop_type": "user",
      "prop": "gp:[Experiment] feature_flag",
      "op": "is",
      "values": ["treatment"]
    }]
  }],
  limit=10
)

With Event Filters

Amplitude:get_session_replays(
  projectId="558383",
  eventCountFilters=[{
    "count": "1",
    "operator": "greater or equal",
    "event": {"event_type": "cobrokelead", "filters": [], "group_by": []}
  }],
  limit=10
)

Response Parsing

CSV Response (isCsvResponse: true)

• Header rows contain metadata
• Data header row has column labels (dates)
• Data rows: label columns + value columns
• Values may be prefixed with \t

JSON Response (isCsvResponse: false)

{
  timeSeries: [[{value: 614}, {value: 1769}]],
  overallSeries: [[{value: 2383}]],
  seriesMetadata: [{segmentIndex: 0, formulaIndex: 0}],
  xValuesForTimeSeries: ["2025-01-01T00:00:00", "2025-01-02T00:00:00"]
}

Reference Documents

• query_patterns.md - Detailed query templates
• events_taxonomy.md - Complete event reference

Tips for Efficient Queries

1. Search before building - Existing charts are pre-optimized
2. Always name queries - Include descriptive name field
3. Use correct project - Production (558383) vs Leads (678364)
4. Limit group by values - Use groupByLimit to control cardinality
5. Exclude incomplete data - Set excludeIncompleteDatapoints: true for accurate trends
6. Reference charts in responses - Always provide links for validation
7. Batch experiment queries - Query 3-5 at a time to avoid timeouts