"MCP tools for monitoring application memory and CPU usage" matching MCP servers:

Governs and automates network monitoring across SolarWinds Orion and Paessler PRTG platforms with a unified MCP server, enabling querying, alert management, and governed writes with audit logging.

Enables interaction with Google Cloud services including billing cost analysis, log querying, and metrics monitoring through natural language commands. Provides comprehensive tools for managing GCP resources, analyzing costs, detecting anomalies, and retrieving operational insights.

Provides read-only visibility into local CPU pools and Apple GPU lane usage inside Claude Code and Codex.

Enables MCP-compatible clients to inspect the local machine's CPU, memory, and disk information through tools, a structured resource, and an upgrade-assessment prompt.

MCP server that wraps Cursor Enterprise API to enable AI agents to answer questions about team spending, usage, and model adoption.

Exposes rate-limit and usage data for AI coding agent CLIs as MCP tools, enabling querying of usage snapshots and reset times.

Enables SSH-based read-only monitoring of Linux vServers via Claude, allowing queries for system metrics, logs, network info, and health check reports.

An MCP server that decodes V8 CPU profiles into token-efficient bottleneck summaries for AI agents, enabling them to analyze CPU consumption, call trees, GC pressure, and async bottlenecks.

Connects AI agents to live observability stacks including Sentry, GitHub, Vercel, Better Stack, and Cloudflare, enabling end-to-end incident investigation, deployment correlation, root cause analysis, and regression triage.

Enables local-first tracking of AI token usage and costs from Claude Code and OpenCode, answering queries about tokens, models, sessions, and cost through MCP tools and a CLI, with no network calls.

Provides Claude AI assistants with focused monitoring tools for n8n workflow execution analysis, including active workflow listing, execution history with KPIs, and detailed failure information.

Enables MCP clients to inspect Oracle Integration Cloud integrations, connections, runtime instances, errors, and flow logs through natural-language queries.

Read-only MCP server for querying AI usage metrics (exact tokens, cost, latency, errors, productivity) from a local SQLite database, enabling charts and analysis in Claude Code and Cursor.

Enables Managed Agent sessions to retrieve provider-specific alarms, metrics, and logs through MCP tools, supporting mock and AWS-backed monitoring data.

Provides telemetry tools for retrieving recent logs and system metrics to support root-cause analysis of infrastructure incidents. Enables autonomous incident triage with grounded verification and human-in-the-loop remediation.

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

Enables AI assistants to access Scout Monitoring performance and error data through Scout's API. Provides traces, errors, metrics, and insights for Rails, Django, FastAPI, Laravel and other applications to help identify and fix performance issues like N+1 queries, slow endpoints, and memory bloat.

An MCP server for managing and monitoring Docker, Docker Compose, and Kubernetes environments alongside Azure Application Insights. It enables advanced log filtering, container lifecycle management, and querying of cloud application traces and metrics.

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.

Presents a plausible, tool-rich workspace surface — file reading, credential listing, command execution, database queries, and configuration retrieval — that returns only synthetic results while logging every connection, tool call, and argument received. It enables researchers to measure what MCP clients and agents actually do when handed dangerous-sounding tools, including whether they follow injected instructions or complete a retrieve-then-use credential chain.