"An overview or resources about Node.js" matching MCP servers:

Enables AI agents to debug and repair historical Solaris/SPARC systems by providing an out-of-band control plane with guest DTrace, QEMU monitor access, SPARC-aware GDB, host eBPF/perf tracing, and an immutable evidence ledger for cross-layer hypothesis testing.

An MCP server that provides offline searchable Microsoft 365 roadmap data and live Azure status, enabling AI agents to answer questions about upcoming features, roadmap changes, and Azure incidents.

Identifies resource-intensive processes on macOS across CPU, memory, and network usage, with tools for detailed process filtering and system overview.

Enables AI assistants to monitor, analyze, and configure OWASP ModSecurity CRS via Docker, with tools for overview, drill-down, and rule/IP management.

Enables observation-only system inspection through MCP, exposing system identity, resources, process summary, and network summary without any mutation or command execution.

Enables querying a self-hosted Plausible Analytics instance to retrieve compressed analytics data such as overview stats, top pages, traffic sources, and trends, with computed deltas and human-readable filters.

An MCP server for interacting with Prefect resources, enabling AI assistants to monitor, manage, and debug workflows.

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The AgentOps MCP server provides access to observability and tracing data for debugging complex AI agent runs. This adds crucial context about where the AI agent succeeds or fails.

Enables an AI assistant to diagnose on-premise Windows pm2-managed Node/Nitro services by answering read-only questions about deployed builds, process liveness, logs, current configuration, and whether the running process started with the configuration currently on disk.

Enables querying privacy-focused website analytics from Plausible's read-only Stats API v2, with tools for overview, timeseries, breakdown, and goal performance metrics.

Local MCP server for Java heap dump analysis with Eclipse MAT: heap overview, histograms, dominator trees, threads, OQL, leak suspects, and OOM diagnosis.

Mnemosyne is an MCP server that gives AI assistants deep visibility into Kubernetes clusters, enabling natural language queries about cluster state, pods, services, and more.

An AI agent system that monitors manufacturing equipment health using the Model Context Protocol (MCP). Enables answering natural language questions about equipment status, maintenance, and anomalies through MCP tools.

Enables SafeOps operators to monitor worker health, inspect job queues, investigate incidents, search runbooks, and approve or reject targeted failed-job retries through MCP tools, resources, and a prompt.

Exposes VMware vROps resources, metrics, and alerts as MCP tools, allowing an LLM client to directly query monitored infrastructure.

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

An MCP server that records agent execution metrics and exposes a Context Window Explorer to visualize exactly what entered the model's context window across sessions, tokens, and tool calls.

Provides cryptographically signed, tamper-evident compute usage receipts via a Model Context Protocol server, exposing tools to record usage, verify receipts, list the ledger, and verify ledger integrity without requiring any account, API key, or single cloud provider.

Enables classification of GPU workloads as training, inference, or idle from telemetry data, with tools for one-shot classification, benchmarking, and audit log verification.