Execute KQL queries and analyze data in Azure Data Explorer for log analytics, telemetry, and time series insights. Execute KQL queries against massive datasets with sub-second performance, including filtering, aggregation, time series analysis, and cross-table joins Discover and explore cluster resources, databases, and table schemas to understand your data model before querying Supports five core query patterns: basic retrieval, aggregation analysis, time series analytics, join-based correlation, and schema discovery Built-in fallback to Azure CLI commands when MCP tools timeout or encounter connection errors

Set up tracing, logging, and monitoring for deployed ADK agents across Cloud Trace, BigQuery, and third-party platforms. Four observability tiers: Cloud Trace (always enabled, distributed tracing), Prompt-Response Logging (GenAI interactions to GCS/BigQuery), BigQuery Agent Analytics (structured agent events), and third-party integrations (AgentOps, Phoenix, MLflow, Weave, Freeplay, and others) For Agent Runtime deployments, run agents-cli infra single-project before first deploy to provision Terraform-managed infrastructure (service account, GCS bucket, BigQuery dataset); post-deployment setup requires manual IAM and env var configuration Cloud Trace works out of the box with OpenTelemetry spans tracking invocation flow, LLM calls, and tool execution; accessible via Cloud Console Trace explorer Prompt-response logging is privacy-preserving by default (metadata only via OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT=NO_CONTENT ); disabled locally unless LOGS_BUCKET_NAME is set Includes troubleshooting guide covering missing traces, privacy misconfiguration, BigQuery setup, and cost optimization strategies

Daily schedule and task summary combining calendar events and pending to-dos for a specified date. Fetches calendar events and incomplete tasks for today, tomorrow, or a custom date range using ISO 8601 timestamps Requires explicit --complete=false flag when querying tasks to exclude completed items from the summary Outputs a structured report with time-converted events, task deadlines, conflict detection, and free time slots Supports filtering by due date ( --due-end ) and pagination ( --page-all ) for large task lists; requires prior Lark CLI authentication with calendar and task scopes

Query historical Azure costs, forecast future spending, and identify optimization opportunities across subscriptions and resource groups. Supports three primary workflows: cost queries with breakdown by service/resource/tag, spending forecasts with configurable time periods, and cost optimization analysis including orphaned resources and VM rightsizing Integrates with Azure Cost Management API (query and forecast endpoints) and Azure Monitor for utilization metrics; includes MCP tools for documentation lookup, CLI command generation, and Azure Quick Review compliance scanning Handles subscription, resource group, management group, and billing account scopes; enforces Cost Management Reader role requirement and includes rate-limit handling (4 requests/minute per scope) Requires actual cost data validation before recommendations; always presents total bill alongside optimization suggestions and includes Azure Portal links for all resources