"A server for finding weather information in China" matching MCP servers:

Provides real-time system metrics and information through a Model Context Protocol interface, enabling access to CPU usage, memory statistics, disk information, network status, and running processes.

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

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.

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.

Enables Claude to discover and understand infrastructure-as-code drift by listing projects with drift, analyzing Terraform plan outputs, and providing remediation information.

An MCP server that enables AI agents to safely operate multi-cluster Kubernetes fleets through an Open Cluster Management hub, with policy enforcement, human approval, and audit tracing.

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

Lets an assistant read recorded production AI agent runs — steps, model and tool calls with arguments, and outcomes — along with the findings of comparison runs that checked a prompt or model change against those recordings, and verify a signed evidence bundle offline with no account or network. It is strictly read-only: no tool starts a replay or comparison, so nothing can spend money or act without a person deciding.

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.

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.

Enables read-only observability of Windows systems, exposing CPU, memory, storage, service, and health information through Model Context Protocol tools.

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

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

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