"A guide to interface components in UI design" matching MCP servers:

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.

An MCP server that provides cost and reliability observability for LLM and agent workflows. It records model calls and allows querying and aggregating telemetry data through MCP tools.

Voice-powered bug reporting MCP server with 13 tools. Captures console logs, network errors, DOM traces, and developer voice descriptions. AI reads structured reports to find and fix bugs automatically. Chrome Extension + Python CLI.

Enables AI agents to monitor Docker containers, system resources, ports, and logs in real-time.

Enables AI assistants to query and analyze AI agent sessions from observability providers like Shepherd (AIOBS) and Langfuse, allowing users to debug agent runs, compare sessions, track performance, and analyze LLM usage patterns.

An MCP server that monitors 12 Australian SMB intelligence sources to analyze market signals and provide structured data for content strategy agents. It enables users to track regulatory changes, AI trends, and business data through automated signal grading and Slack integration.

Enables auditing and monitoring of AI agent MCP tool calls, with behavioral anomaly detection, lab-scoped red-team simulation, and Telegram notifications.

Manage and monitor Hadoop clusters via Apache Ambari API, enabling service operations, configuration changes, status checks, and request tracking through a unified MCP interface for simplified administration. * Guide: https://call518.medium.com/llm-based-ambari-control-via-mcp-8668a2b5ffb9

MCP Server for notify to telegram / weixin

Read-only MCP server for TrueNAS Scale debugging, providing k3s tools for kubectl operations and whitelisted midclt calls for system info, apps, and pools.

A command-line interface and MCP server for managing and monitoring multiple development services through a unified terminal UI. It enables users to interact with service logs, filter messages, and control development environments directly via Model Context Protocol integration.

A local MCP server for AI agents to log activities, query logs, and leave notes for each other, featuring a web UI and REST API.

Records, replays, and correlates visual and API events in the browser, enabling AI assistants to understand which API calls feed which UI elements.

A centralized server that tracks and manages connected agents, providing a web interface to monitor their status while enabling agent communication through a central point.

The MCP server provides an interface to the Datadog API, enabling seamless management of incidents, monitoring, logs, dashboards, metrics, traces, and hosts. Its extensible design allows easy integration of additional Datadog APIs for future expansions.

Enables monitoring and querying of telecom performance management (PM) counters from remote SFTP locations, providing access to interface statistics, CPU/memory utilization, BGP peer data, and system metrics through a conversational interface.

Enables users to generate and execute InfluxDB queries using natural language commands in Korean. Supports querying metrics like CPU usage, memory status, and system monitoring data through conversational interface.

Enables real-time Kubernetes cluster observability and debugging through standardized MCP interface. Provides access to pods, services, nodes, events, and includes built-in tools for cluster health analysis and issue identification.

Streams live React profiler data to AI agents, enabling them to analyze performance and suggest specific, actionable fixes for slow components.

Automates three core workflows for Sangfor Engineer: project all-in-one (Excel to guide to validation to report), equipment health check, and Obsidian wiki integration.