Datadog Automation

Datadog Automation via Rube MCP

Automate Datadog monitoring and observability operations through Composio's Datadog toolkit via Rube MCP.

Prerequisites

• Rube MCP must be connected (RUBE_SEARCH_TOOLS available)
• Active Datadog connection via RUBE_MANAGE_CONNECTIONS with toolkit datadog
• Always call RUBE_SEARCH_TOOLS first to get current tool schemas

Setup

Get Rube MCP: Add https://rube.app/mcp as an MCP server in your client configuration. No API keys needed — just add the endpoint and it works.

1. Verify Rube MCP is available by confirming RUBE_SEARCH_TOOLS responds
2. Call RUBE_MANAGE_CONNECTIONS with toolkit datadog
3. If connection is not ACTIVE, follow the returned auth link to complete Datadog authentication
4. Confirm connection status shows ACTIVE before running any workflows

Core Workflows

1. Query and Explore Metrics

When to use: User wants to query metric data or list available metrics

Tool sequence:

1. DATADOG_LIST_METRICS - List available metric names [Optional]
2. DATADOG_QUERY_METRICS - Query metric time series data [Required]

Key parameters:

• query: Datadog metric query string (e.g., avg:system.cpu.user{host:web01})
• from: Start timestamp (Unix epoch seconds)
• to: End timestamp (Unix epoch seconds)
• q: Search string for listing metrics

Pitfalls:

• Query syntax follows Datadog's metric query format: aggregation:metric_name{tag_filters}
• from and to are Unix epoch timestamps in seconds, not milliseconds
• Valid aggregations: avg, sum, min, max, count
• Tag filters use curly braces: {host:web01,env:prod}
• Time range should not exceed Datadog's retention limits for the metric type

2. Search and Analyze Logs

When to use: User wants to search log entries or list log indexes

Tool sequence:

1. DATADOG_LIST_LOG_INDEXES - List available log indexes [Optional]
2. DATADOG_SEARCH_LOGS - Search logs with query and filters [Required]

Key parameters:

• query: Log search query using Datadog log query syntax
• from: Start time (ISO 8601 or Unix timestamp)
• to: End time (ISO 8601 or Unix timestamp)
• sort: Sort order ('asc' or 'desc')
• limit: Number of log entries to return

Pitfalls:

• Log queries use Datadog's log search syntax: service:web status:error
• Search is limited to retained logs within the configured retention period
• Large result sets require pagination; check for cursor/page tokens
• Log indexes control routing and retention; filter by index if known

3. Manage Monitors

When to use: User wants to create, update, mute, or inspect monitors

Tool sequence:

1. DATADOG_LIST_MONITORS - List all monitors with filters [Required]
2. DATADOG_GET_MONITOR - Get specific monitor details [Optional]
3. DATADOG_CREATE_MONITOR - Create a new monitor [Optional]
4. DATADOG_UPDATE_MONITOR - Update monitor configuration [Optional]
5. DATADOG_MUTE_MONITOR - Silence a monitor temporarily [Optional]
6. DATADOG_UNMUTE_MONITOR - Re-enable a muted monitor [Optional]

Key parameters:

• monitor_id: Numeric monitor ID
• name: Monitor display name
• type: Monitor type ('metric alert', 'service check', 'log alert', 'query alert', etc.)
• query: Monitor query defining the alert condition
• message: Notification message with @mentions
• tags: Array of tag strings
• thresholds: Alert threshold values (critical, warning, ok)

Pitfalls:

• Monitor type must match the query type; mismatches cause creation failures
• message supports @mentions for notifications (e.g., @slack-channel, @pagerduty)
• Thresholds vary by monitor type; metric monitors need critical at minimum
• Muting a monitor suppresses notifications but the monitor still evaluates
• Monitor IDs are numeric integers

4. Manage Dashboards

When to use: User wants to list, view, update, or delete dashboards

Tool sequence:

1. DATADOG_LIST_DASHBOARDS - List all dashboards [Required]
2. DATADOG_GET_DASHBOARD - Get full dashboard definition [Optional]
3. DATADOG_UPDATE_DASHBOARD - Update dashboard layout or widgets [Optional]
4. DATADOG_DELETE_DASHBOARD - Remove a dashboard (irreversible) [Optional]

Key parameters:

• dashboard_id: Dashboard identifier string
• title: Dashboard title
• layout_type: 'ordered' (grid) or 'free' (freeform positioning)
• widgets: Array of widget definition objects
• description: Dashboard description

Pitfalls:

• Dashboard IDs are alphanumeric strings (e.g., 'abc-def-ghi'), not numeric
• layout_type cannot be changed after creation; must recreate the dashboard
• Widget definitions are complex nested objects; get existing dashboard first to understand structure
• DELETE is permanent; there is no undo

5. Create Events and Manage Downtimes

When to use: User wants to post events or schedule maintenance downtimes

Tool sequence:

1. DATADOG_LIST_EVENTS - List existing events [Optional]
2. DATADOG_CREATE_EVENT - Post a new event [Required]
3. DATADOG_CREATE_DOWNTIME - Schedule a maintenance downtime [Optional]

Key parameters for events:

• title: Event title
• text: Event body text (supports markdown)
• alert_type: Event severity ('error', 'warning', 'info', 'success')
• tags: Array of tag strings

Key parameters for downtimes:

• scope: Tag scope for the downtime (e.g., host:web01)
• start: Start time (Unix epoch)
• end: End time (Unix epoch; omit for indefinite)
• message: Downtime description
• monitor_id: Specific monitor to downtime (optional, omit for scope-based)

Pitfalls:

• Event text supports Datadog's markdown format including @mentions
• Downtimes scope uses tag syntax: host:web01, env:staging
• Omitting end creates an indefinite downtime; always set an end time for maintenance
• Downtime monitor_id narrows to a single monitor; scope applies to all matching monitors

6. Manage Hosts and Traces

When to use: User wants to list infrastructure hosts or inspect distributed traces

Tool sequence:

1. DATADOG_LIST_HOSTS - List all reporting hosts [Required]
2. DATADOG_GET_TRACE_BY_ID - Get a specific distributed trace [Optional]

Key parameters:

• filter: Host search filter string
• sort_field: Sort hosts by field (e.g., 'name', 'apps', 'cpu')
• sort_dir: Sort direction ('asc' or 'desc')
• trace_id: Distributed trace ID for trace lookup

Pitfalls:

• Host list includes all hosts reporting to Datadog within the retention window
• Trace IDs are long numeric strings; ensure exact match
• Hosts that stop reporting are retained for a configured period before removal

Common Patterns

Monitor Query Syntax

Metric alerts:

avg(last_5m):avg:system.cpu.user{env:prod} > 90

Log alerts:

logs("service:web status:error").index("main").rollup("count").last("5m") > 10

Tag Filtering

• Tags use key:value format: host:web01, env:prod, service:api
• Multiple tags: {host:web01,env:prod} (AND logic)
• Wildcard: host:web*

Pagination

• Use page and page_size or offset-based pagination depending on endpoint
• Check response for total count to determine if more pages exist
• Continue until all results are retrieved

Known Pitfalls

Timestamps:

• Most endpoints use Unix epoch seconds (not milliseconds)
• Some endpoints accept ISO 8601; check tool schema
• Time ranges should be reasonable (not years of data)

Query Syntax:

• Metric queries: aggregation:metric{tags}
• Log queries: field:value pairs
• Monitor queries vary by type; check Datadog documentation

Rate Limits:

• Datadog API has per-endpoint rate limits
• Implement backoff on 429 responses
• Batch operations where possible

Quick Reference

When to Use

This skill is applicable to execute the workflow or actions described in the overview.

AGI Framework Integration

Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills

Memory-First Protocol

Cache data schemas, transformation rules, and query patterns. BM25 excels at finding specific column names, table references, and SQL patterns.

# Check for prior data engineering context before starting
python3 execution/memory_manager.py auto --query "data processing patterns and pipeline configurations for Datadog Automation"

Storing Results

After completing work, store data engineering decisions for future sessions:

python3 execution/memory_manager.py store \
  --content "Data pipeline: ETL from PostgreSQL to Qdrant, 50K records/batch, incremental sync via updated_at" \
  --type technical --project <project> \
  --tags datadog-automation data

Multi-Agent Collaboration

Share data schema changes with backend and frontend agents so they update their models accordingly.

python3 execution/cross_agent_context.py store \
  --agent "<your-agent>" \
  --action "Data pipeline implemented — ETL processing with validation, deduplication, and error recovery" \
  --project <project>