"Recommendations for MCP with Encapsulated UI Component Library" matching MCP servers:

A Model Context Protocol server that provides AI assistants access to AWS CloudWatch Logs, enabling browsing, searching, summarizing, and correlating logs across multiple AWS services.

Enables LLMs to interact with Kibana through composable toolboxes for building dashboards, managing data, and handling alerts, among other operations.

Enables AI agents to interact seamlessly with Splunk environments through 20+ tools for search, analytics, data discovery, administration, and health monitoring. Features AI-powered troubleshooting workflows and supports multiple Splunk instances with production-ready security.

Enables querying and analyzing Falco security events from Falcosidekick UI through MCP tools. Supports filtering events by time windows and retrieving full event details for security monitoring and incident investigation.

Enables AI-driven SOC investigations by providing automated Splunk querying, threat intelligence enrichment, and response actions through natural language. Includes tools for IP pivoting, lateral movement detection, and label harvesting.

A Model Context Protocol server that provides seamless integration with Coroot observability platform, enabling monitoring of applications, analysis of performance metrics, examination of logs and traces, and management of infrastructure through Coroot's comprehensive API.

An MCP server implementation that integrates AI assistants with Langfuse workspaces, allowing models to query LLM metrics by time range.

Provides a standardized way for MCP clients to interact with Apache Airflow's REST API, supporting operations like DAG management and monitoring Airflow system health.

MCP server that integrates with VictoriaMetrics vmanomaly API to enable AI-assisted anomaly detection, model management, and configuration generation.

Enables AI clients like Claude to triage, investigate, and operate Icinga installations through natural language, integrating with Icinga's REST APIs and providing deep awareness of monitoring plugins and historical performance data.

Enables an AI assistant to diagnose a broken application on a local Kubernetes cluster by inspecting pods, events, and logs, with server-side guardrails, least-privilege RBAC, and an audit log. It can also restart deployments in allowed namespaces only after a dry-run preview and host approval.

A safety layer and monitoring copilot for AI agents using Splunk MCP, recording tool calls, enforcing policies, and blocking risky actions.

Enables natural language analysis of network packet captures, including protocol detection, flow analysis, and security threat identification, integrated with AI assistants.

Provides tools to search Splunk logs, inject test data, and send triage emails, enabling AI-driven incident investigation.

Provides Model Context Protocol access to the Dynatrace platform API, letting AI agents run live queries against observability, security, and platform data and manage a Dynatrace environment. It is hosted remotely by Dynatrace, so MCP hosts can add it by name from the official registry without installing anything.

A remote, Dynatrace-hosted MCP server that gives AI agents live API access to your Dynatrace environment. It enables querying and analyzing observability, security, and platform data — such as DQL queries, logs, traces, problems, and dashboards — so agents can act on production context instead of static knowledge.

A lightweight npm MCP server for deep Redis diagnostics, offering tools for memory, slowlog, clients, keyspace, latency, and config analysis with AI-powered recommendations.

MCP server for AI agent deployment health — gateway status, CPU/memory/swap, recent errors from journalctl/dmesg, skill registry integrity, upgrade outcomes, cron + disk usage. Each component gets HEALTHY/DEGRADED/CRITICAL classification with overall rollup.

A comprehensive, AI-powered performance analysis and monitoring platform for OpenShift/Kubernetes clusters. This project provides Model Context Protocol (MCP) servers for analyzing etcd, network, and OVN-Kubernetes components with deep performance insights, automated root cause analysis, and actionable recommendations.

Enables AI assistants to monitor and analyze industrial process data from DCS/SCADA systems via MCP protocol, with domain expert knowledge for anomaly detection and recommendations.

Enables users to ask about render performance in React and React Native apps via natural language, exposing tools for listing render hotspots and explaining component re-renders.

Enables infrastructure health overviews, detailed host metrics, active alert retrieval, and optimization recommendations through MCP tools.

This server runs inside an Rsbuild or Rspack dev server to expose MCP tools for inspecting and editing live Vue component state, component trees, Vue Router info, and Pinia stores.

Comprehensive system diagnostics and monitoring server providing detailed hardware information, performance metrics, and optimization recommendations.

Enables comprehensive analysis of Apache Spark event logs from S3, HTTP, or local sources, providing performance metrics, resource monitoring, shuffle analysis, and automated optimization recommendations with interactive HTML reports.

Enables AI assistants to inspect and monitor PostgreSQL databases with read-only DBA tools for performance analysis, bloat detection, locks/deadlocks, autovacuum, schema discovery, replication, and maintenance recommendations.

Monitors ML models in production for data drift and performance degradation, providing automated alerts and retraining recommendations.

Enables AI assistants to perform PostgreSQL operations and monitoring through natural language, including performance analysis, bloat detection, lock/deadlock monitoring, autovacuum inspection, schema discovery, and maintenance recommendations with production-safe read-only tools across PostgreSQL 12–18.

An MCP server for querying Grafana Loki directly with a discovery-first workflow — labels, values, series, and LogQL queries without requiring Grafana.

Enables querying logs and metrics with plain SQL from any MCP client via pooml's read-only query API.