Update REAMDE.md - Features

Author: call518

README.md

@@ -31,18 +31,18 @@
 
 ### Features
 
-- **Interactive Ambari Operations Hub** – Provides an MCP-based foundation for querying and managing services through natural language instead of console or UI interfaces.
-- **Real-time Cluster Visibility** – Comprehensive view of key metrics including service status, host details, alert history, and ongoing requests in a single interface.
-- **Metrics Intelligence Pipeline** – Dynamically discovers and filters AMS appIds and metric names, connecting directly to time-series analysis workflows.
-- **Automated Operations Workflow** – Consolidates repetitive start/stop operations, configuration checks, user queries, and request tracking into consistent scenarios.
-- **Built-in Operational Reports** – Instantly delivers dfsadmin-style HDFS reports, service summaries, and capacity metrics through LLM or CLI interfaces.
-- **Safety Guards and Guardrails** – Requires user confirmation before large-scale operations and provides clear guidance for risky commands through prompt templates.
-- **LLM Integration Optimization** – Includes natural language examples, parameter mapping, and usage guides to ensure stable AI agent operations.
-- **Flexible Deployment Models** – Supports stdio/streamable-http transport, Docker Compose, and token authentication for deployment across development and production environments.
-- **Performance-Oriented Caching Architecture** – Built-in AMS metadata cache and request logging ensure fast responses even in large-scale clusters.
-- **Scalable Code Architecture** – Asynchronous HTTP, structured logging, and modularized tool layers enable easy addition of new features.
-- **Production-Validated** – Based on tools validated in test Ambari clusters, ready for immediate use in production environments.
-- **Diversified Deployment Channels** – Available through PyPI packages, Docker images, and other preferred deployment methods.
+- ✅ **Interactive Ambari Operations Hub** – Provides an MCP-based foundation for querying and managing services through natural language instead of console or UI interfaces.
+- ✅ **Real-time Cluster Visibility** – Comprehensive view of key metrics including service status, host details, alert history, and ongoing requests in a single interface.
+- ✅ **Metrics Intelligence Pipeline** – Dynamically discovers and filters AMS appIds and metric names, connecting directly to time-series analysis workflows.
+- ✅ **Automated Operations Workflow** – Consolidates repetitive start/stop operations, configuration checks, user queries, and request tracking into consistent scenarios.
+- ✅ **Built-in Operational Reports** – Instantly delivers dfsadmin-style HDFS reports, service summaries, and capacity metrics through LLM or CLI interfaces.
+- ✅ **Safety Guards and Guardrails** – Requires user confirmation before large-scale operations and provides clear guidance for risky commands through prompt templates.
+- ✅ **LLM Integration Optimization** – Includes natural language examples, parameter mapping, and usage guides to ensure stable AI agent operations.
+- ✅ **Flexible Deployment Models** – Supports stdio/streamable-http transport, Docker Compose, and token authentication for deployment across development and production environments.
+- ✅ **Performance-Oriented Caching Architecture** – Built-in AMS metadata cache and request logging ensure fast responses even in large-scale clusters.
+- ✅ **Scalable Code Architecture** – Asynchronous HTTP, structured logging, and modularized tool layers enable easy addition of new features.
+- ✅ **Production-Validated** – Based on tools validated in test Ambari clusters, ready for immediate use in production environments.
+- ✅ **Diversified Deployment Channels** – Available through PyPI packages, Docker images, and other preferred deployment methods.
 
 ### Docuement for Airflow REST-API