"Information on memory" matching MCP servers:

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

A Linux system monitoring MCP server that provides real-time information on CPU, memory, disk, network, processes, Docker, security, and more via MCP tools.

Provides system hardware information (CPU, memory, storage, etc.) and system status (OS, network, resource usage) via MCP tools.

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.

A lightweight server that provides real-time system information including CPU, memory, disk, and GPU statistics for monitoring and diagnostic purposes.

MCP server for On-Call Health burnout analysis. Connects AI assistants to your on-call data for workload insights.

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

Provides structured MCP tools for local runtime observability and debugging of React Native/Expo apps on Android, enabling evidence-first diagnostics such as screenshots, UI trees, per-request network capture, performance metrics, and CDP-based console/heap/profiling exports.

Enables AI assistants to inspect React Native app performance including memory, rendering, frames, and network requests, providing plain-language summaries and shareable reports.

Provides read-only access to Tactical RMM, enabling agent searches, detailed agent information, audit log queries, and execution of information-gathering PowerShell scripts on agents, with Entra ID authentication and a strict read-only guard.

Enables comprehensive system monitoring and diagnostics through 18 tools that provide detailed information about CPU, memory, disk usage, network interfaces, running processes, battery status, hardware details, and temperature monitoring. Allows users to query system information and performance metrics through natural language interactions.

Provides persistent shared memory and multi-agent coordination for MCP-compatible coding agents on Zerops, with a live Observatory dashboard for monitoring activity.

Enables AI clients to access real-time system information such as memory and swap usage from a Linux machine via MCP tools.

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.

8 standalone MCP servers for SMBs — Guardrails (prompt injection, PII, secrets detection), FinOps (AI cost tracking and budgets), Observability (agent tracing), Smart Router (17+ models, 9 providers), Trust Score (governance scoring 0-100), Memory (persistent agent memory), ThinkSecure (runtime security + SOC2/GDPR audit), and A2A Lite (agent-to-agent protocol). 37 tools total, TNC credits

Provides MCP tools to create and query SigNoz trace funnels, enabling agents to measure their own step conversion rates from within the agent loop.

MCP server exposing rlg (RustLogs) log files as tools for on-call / SRE agent workflows. Three tools over JSON-RPC stdio — tail_log (last N events, filtered by level/target), filter_log (predicate filtering), summarize_errors (grouped error taxonomy). Pure Rust. MIT OR Apache-2.0.

Enables log analysis for Tenable Patch Management (SaaS and on-prem) by parsing, de-duplicating, and decoding server/client logs into structured verdicts, ranked issues, and known-issue fixes.

Read-only MCP server for searching and aggregating logs from VMware Aria Operations for Logs (formerly vRealize Log Insight). Enables querying log events, aggregating with spike detection, and retrieving alert information.

Enables an LLM agent to investigate a Kong Gateway through fourteen read-only, schema-validated tools and a deterministic diagnostic engine, so it can trace routes, services, plugins and upstream health and explain faults such as why authentication is failing on a path. Every fact comes from the gateway itself, and the model only selects tools and writes the evidence-backed explanation.