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Uses native OS APIs (via Node.js child processes or system libraries) to poll system state at configurable intervals, then serializes metrics into structured JSON payloads that LLM clients can query synchronously or subscribe to via MCP's resource subscription mechanism.","intents":["I want my AI agent to monitor system health and make decisions based on real-time CPU/memory usage","I need to expose host metrics to Claude or other LLM clients without building a custom HTTP API","I want to trigger alerts or actions when system resources exceed thresholds"],"best_for":["AI agents running on-device that need environmental awareness","Teams building observability layers into MCP-based AI systems","Developers prototyping autonomous systems that respond to resource constraints"],"limitations":["Metrics are point-in-time snapshots; no built-in time-series storage or historical aggregation","Polling-based approach adds latency between metric collection and LLM visibility (typically 1-5 second intervals)","Limited to metrics available via Node.js os module; advanced profiling (flame graphs, kernel traces) not supported","No built-in filtering or sampling — all metrics sent to client on every query, which may be verbose for high-frequency polling"],"requires":["Node.js 16+ (MCP SDK compatibility)","Unix-like OS or Windows with Node.js process APIs exposed","MCP client capable of calling tools (Claude Desktop, custom MCP client, etc.)"],"input_types":["query parameters (optional: metric type filter, time window)","subscription requests (MCP resource subscriptions)"],"output_types":["JSON object with CPU %, memory usage (bytes/%), disk I/O, process list","structured metric events via MCP resource updates"],"categories":["tool-use-integration","observability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-modelcontextprotocolserver-system-monitor__cap_1","uri":"capability://tool.use.integration.mcp.tool.schema.generation.for.system.metrics","name":"mcp tool schema generation for system metrics","description":"Automatically generates MCP-compliant tool definitions (JSON schemas) for each system metric endpoint, enabling LLM clients to discover and call metric-fetching functions with proper type hints and descriptions. The server introspects available metrics at startup and generates OpenAPI-style schemas that describe input parameters (e.g., process filter, metric type) and output structures, which are then advertised via MCP's tools/list endpoint.","intents":["I want Claude to understand what system metrics are available without me manually documenting them","I need type-safe metric queries so the LLM doesn't request invalid metric combinations","I want the LLM to auto-complete metric names and understand parameter constraints"],"best_for":["Developers building LLM agents that need discoverable system APIs","Teams using Claude Desktop or other MCP clients that render tool schemas","Builders prototyping multi-tool agents where metric queries are one of many capabilities"],"limitations":["Schema generation is static at server startup; dynamic metrics added at runtime won't be reflected until server restart","Schemas describe only the metric interface, not the semantic meaning or interpretation (e.g., what 'high CPU' means in context)","No schema versioning or deprecation support — breaking changes to metric output require client-side updates"],"requires":["MCP client that supports tool schema discovery (tools/list endpoint)","Node.js 16+"],"input_types":["metric type identifiers (strings)","optional filter parameters (process name, threshold)"],"output_types":["JSON schema objects (OpenAPI 3.0 compatible)","MCP tools/list response with tool definitions"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-modelcontextprotocolserver-system-monitor__cap_2","uri":"capability://data.processing.analysis.process.level.resource.attribution.and.filtering","name":"process-level resource attribution and filtering","description":"Breaks down system metrics to the individual process level, allowing LLM clients to query CPU, memory, and I/O usage per process, with optional filtering by process name, PID, or resource threshold. Internally uses Node.js child processes to invoke system commands (ps, top, or equivalent) and parses their output into structured process records, then applies filter logic to return only relevant processes.","intents":["I want to know which specific process is consuming the most CPU right now","I need to find all processes matching a pattern (e.g., all Node.js processes) and their resource usage","I want to trigger an action if a particular process exceeds a memory threshold"],"best_for":["Agents managing multi-process systems (microservices, worker pools)","Developers debugging resource leaks in running applications","Teams building auto-scaling or process-restart logic triggered by LLM decisions"],"limitations":["Process enumeration is OS-specific; Windows, macOS, and Linux have different ps/tasklist output formats, requiring platform-specific parsing","Parsing system command output is fragile — changes to OS utilities or locale settings can break parsing","Process snapshots are point-in-time; no built-in per-process history or trend detection","Cannot directly access process internals (heap dumps, thread counts) without additional system tools"],"requires":["Unix-like OS (Linux, macOS) with ps command, or Windows with tasklist/Get-Process","Node.js 16+","Sufficient permissions to enumerate all processes (may require elevated privileges on some systems)"],"input_types":["process name filter (string, regex optional)","PID filter (integer)","resource threshold (CPU %, memory bytes)"],"output_types":["JSON array of process objects with {pid, name, cpu%, memory_bytes, memory%, command}","filtered subset matching criteria"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-modelcontextprotocolserver-system-monitor__cap_3","uri":"capability://automation.workflow.configurable.metric.polling.and.update.intervals","name":"configurable metric polling and update intervals","description":"Allows configuration of how frequently the server collects system metrics (e.g., every 1 second, 5 seconds, or on-demand) and how long metrics are cached before being refreshed. Implements a polling loop that runs at a configurable interval, stores the latest snapshot in memory, and serves cached results to clients until the next poll cycle completes. Configuration is typically provided via environment variables or a config file at server startup.","intents":["I want to balance metric freshness with CPU overhead — poll every 5 seconds instead of continuously","I need on-demand metric queries without background polling overhead","I want to tune the server's resource footprint for resource-constrained environments"],"best_for":["Operators deploying the server on edge devices or embedded systems with limited CPU","Teams with strict latency requirements who need to tune polling intervals","Developers prototyping different monitoring strategies without code changes"],"limitations":["Polling interval is global; cannot configure per-metric intervals (e.g., CPU every 1s, disk every 10s)","Cached metrics may be stale if a client queries just before a poll cycle; maximum staleness is one interval","No adaptive polling based on system load or client demand — interval is static","Configuration changes require server restart; no hot-reload support"],"requires":["Node.js 16+","Environment variable or config file support (implementation-dependent)"],"input_types":["polling interval (milliseconds or seconds)","cache TTL (time-to-live)"],"output_types":["cached metric snapshot (JSON)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-modelcontextprotocolserver-system-monitor__cap_4","uri":"capability://tool.use.integration.mcp.resource.subscription.for.metric.streaming","name":"mcp resource subscription for metric streaming","description":"Implements MCP's resource subscription mechanism to enable clients to subscribe to metric updates and receive push-based notifications when metrics change, rather than polling. The server maintains a list of active subscriptions and pushes updated metric snapshots to subscribed clients at each polling interval or when metrics exceed configured thresholds. Uses MCP's resources/subscribe and resources/updated endpoints to manage subscriptions and deliver updates.","intents":["I want my agent to be notified immediately when CPU usage spikes, not wait for the next poll","I need to stream metrics to multiple clients without each client polling independently","I want to reduce network traffic by only sending metric updates when values change significantly"],"best_for":["Real-time monitoring agents that need low-latency metric updates","Multi-client systems where broadcasting metrics is more efficient than individual polling","Teams building reactive systems that trigger actions on metric thresholds"],"limitations":["Requires MCP client support for resource subscriptions; not all MCP clients implement this feature","Subscription state is in-memory; subscriptions are lost if the server restarts","No built-in filtering at subscription time — server sends all metric updates to all subscribers (clients must filter)","Threshold-based updates require server-side threshold configuration, which may not match client needs"],"requires":["MCP client that supports resources/subscribe endpoint","Network connectivity between server and client (subscriptions require persistent connection or polling fallback)","Node.js 16+"],"input_types":["subscription request with resource URI (e.g., 'metrics://cpu')","optional threshold parameters"],"output_types":["metric update events (JSON) pushed to client","subscription confirmation"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-modelcontextprotocolserver-system-monitor__cap_5","uri":"capability://data.processing.analysis.disk.i.o.and.storage.metrics.collection","name":"disk i/o and storage metrics collection","description":"Collects and exposes disk-level metrics including I/O throughput (read/write bytes per second), I/O operations per second (IOPS), disk utilization percentage, and available/used space per filesystem. Internally queries the OS filesystem APIs (via df, iostat, or equivalent) and parses output into structured disk metrics, optionally tracking I/O deltas between polling intervals to compute throughput.","intents":["I want to monitor disk I/O performance and detect bottlenecks in my application","I need to track available disk space and trigger alerts when storage is running low","I want to understand which filesystems are under heavy I/O load"],"best_for":["Agents managing storage-intensive workloads (databases, file servers, data pipelines)","Teams monitoring disk health and capacity planning","Developers debugging I/O-related performance issues"],"limitations":["I/O metrics are aggregate across all processes; cannot attribute I/O to specific processes without additional tools (lsof, iotop)","Throughput calculation requires two snapshots; first poll cycle may not have baseline for delta computation","Filesystem-level metrics may not reflect actual device-level I/O (e.g., RAID, LVM abstractions)","Windows support may be limited due to different disk metric APIs (Get-Volume vs df)"],"requires":["Unix-like OS with df and optionally iostat, or Windows with Get-Volume","Node.js 16+","Sufficient permissions to query filesystem stats"],"input_types":["filesystem filter (mount point, device name)","metric type filter (IOPS, throughput, utilization)"],"output_types":["JSON object with {filesystem, mount_point, total_bytes, used_bytes, available_bytes, utilization%, read_bytes_per_sec, write_bytes_per_sec, iops}"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-modelcontextprotocolserver-system-monitor__cap_6","uri":"capability://data.processing.analysis.network.interface.metrics.and.connection.tracking","name":"network interface metrics and connection tracking","description":"Collects network interface statistics including bytes sent/received, packet counts, error rates, and optionally tracks active network connections (TCP/UDP sockets) with their associated processes. Queries OS network APIs (via ifconfig, netstat, ss, or equivalent) and parses output into structured network metrics, optionally computing throughput deltas between polling intervals.","intents":["I want to monitor network throughput and detect bandwidth saturation","I need to track which processes are making network connections and their traffic volume","I want to detect network errors or packet loss that might indicate connectivity issues"],"best_for":["Agents managing networked services (APIs, microservices, distributed systems)","Teams monitoring network health and detecting connectivity issues","Developers debugging network-related performance problems"],"limitations":["Connection tracking requires parsing netstat/ss output, which is OS-specific and fragile","Cannot distinguish between different types of traffic (HTTP, DNS, etc.) without packet inspection","Throughput metrics are aggregate across all interfaces; cannot isolate traffic by application without additional tools","Active connection list may be large in high-traffic systems, consuming memory and parsing time","Windows netstat output format differs significantly from Unix, requiring platform-specific parsing"],"requires":["Unix-like OS with ifconfig/ip and netstat/ss, or Windows with Get-NetAdapter/Get-NetTCPConnection","Node.js 16+","Sufficient permissions to query network stats (may require elevated privileges on some systems)"],"input_types":["interface filter (interface name, e.g., 'eth0')","connection filter (process name, port, protocol)"],"output_types":["JSON object with {interface, bytes_sent, bytes_received, packets_sent, packets_received, errors, dropped}","JSON array of active connections with {protocol, local_addr, local_port, remote_addr, remote_port, state, process_name, pid}"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-modelcontextprotocolserver-system-monitor__cap_7","uri":"capability://data.processing.analysis.memory.usage.breakdown.by.category","name":"memory usage breakdown by category","description":"Provides detailed memory usage breakdown including resident set size (RSS), heap usage, external memory, and optionally distinguishes between different memory types (physical, swap, cached). On Linux, parses /proc/meminfo and /proc/[pid]/status for detailed memory accounting; on other OSes, uses available APIs to approximate breakdown. Exposes both system-wide memory and per-process memory details.","intents":["I want to understand whether my application is memory-bound or I/O-bound","I need to detect memory leaks by tracking heap growth over time","I want to know how much memory is available for new processes"],"best_for":["Developers debugging memory-intensive applications","Teams monitoring long-running services for memory leaks","Agents making resource allocation decisions based on available memory"],"limitations":["Memory breakdown is OS-specific; Linux provides detailed /proc stats, but macOS and Windows have different APIs with less granularity","Heap usage is approximate on non-instrumented processes; accurate heap profiling requires language-specific tools (Node.js --inspect, Python tracemalloc)","Swap usage may not be available on all systems (e.g., systems without swap configured)","Per-process memory accounting may double-count shared libraries and memory-mapped files"],"requires":["Linux with /proc filesystem, or macOS/Windows with equivalent memory APIs","Node.js 16+","Sufficient permissions to read /proc/[pid]/status on Linux"],"input_types":["process filter (PID or name)","memory type filter (heap, RSS, swap)"],"output_types":["JSON object with {total_memory, available_memory, used_memory, swap_total, swap_used, cached_memory}","JSON object per process with {rss_bytes, heap_bytes, external_bytes, memory_percent}"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-modelcontextprotocolserver-system-monitor__cap_8","uri":"capability://data.processing.analysis.cpu.usage.and.load.average.tracking","name":"cpu usage and load average tracking","description":"Collects CPU metrics including per-core CPU usage percentage, system-wide CPU average, and load averages (1-minute, 5-minute, 15-minute). Computes CPU usage by reading /proc/stat on Linux or equivalent APIs on other OSes, calculating the delta between consecutive snapshots to derive per-core and aggregate CPU percentages. Exposes both instantaneous CPU usage and historical load averages.","intents":["I want to detect CPU bottlenecks and know if the system is CPU-bound","I need to make scaling decisions based on CPU load trends","I want to understand CPU usage distribution across cores"],"best_for":["Agents managing compute-intensive workloads or auto-scaling systems","Teams monitoring CPU health and detecting performance degradation","Developers profiling CPU-intensive applications"],"limitations":["CPU percentage calculation requires two snapshots; first poll cycle may not have baseline for delta computation","Load average is OS-computed and may not reflect actual CPU utilization on systems with many cores","Per-core CPU usage may be noisy on systems with dynamic frequency scaling or power management","CPU metrics do not distinguish between user, system, and I/O wait time without additional parsing"],"requires":["Linux with /proc/stat, or macOS/Windows with equivalent CPU APIs","Node.js 16+","Sufficient permissions to read /proc/stat on Linux"],"input_types":["core filter (specific core index, or 'all')","metric type filter (usage%, load average)"],"output_types":["JSON object with {cpu_count, cpu_usage_percent, load_1min, load_5min, load_15min}","JSON array of per-core usage percentages"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":27,"verified":false,"data_access_risk":"high","permissions":["Node.js 16+ (MCP SDK compatibility)","Unix-like OS or Windows with Node.js process APIs exposed","MCP client capable of calling tools (Claude Desktop, custom MCP client, etc.)","MCP client that supports tool schema discovery (tools/list endpoint)","Node.js 16+","Unix-like OS (Linux, macOS) with ps command, or Windows with tasklist/Get-Process","Sufficient permissions to enumerate all processes (may require elevated privileges on some systems)","Environment variable or config file support (implementation-dependent)","MCP client that supports resources/subscribe endpoint","Network connectivity between server and client (subscriptions require persistent connection or polling fallback)"],"failure_modes":["Metrics are point-in-time snapshots; no built-in time-series storage or historical aggregation","Polling-based approach adds latency between metric collection and LLM visibility (typically 1-5 second intervals)","Limited to metrics available via Node.js os module; advanced profiling (flame graphs, kernel traces) not supported","No built-in filtering or sampling — all metrics sent to client on every query, which may be verbose for high-frequency polling","Schema generation is static at server startup; dynamic metrics added at runtime won't be reflected until server restart","Schemas describe only the metric interface, not the semantic meaning or interpretation (e.g., what 'high CPU' means in context)","No schema versioning or deprecation support — breaking changes to metric output require client-side updates","Process enumeration is OS-specific; Windows, macOS, and Linux have different ps/tasklist output formats, requiring platform-specific parsing","Parsing system command output is fragile — changes to OS utilities or locale settings can break parsing","Process snapshots are point-in-time; no built-in per-process history or trend detection","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.28,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.6,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:23.904Z","last_scraped_at":"2026-05-03T14:23:45.316Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=npm-modelcontextprotocolserver-system-monitor","compare_url":"https://unfragile.ai/compare?artifact=npm-modelcontextprotocolserver-system-monitor"}},"signature":"1mXVHBIX1Wh4iPm7oownjmUb5twzJMFJGyKWgL8gyoRC5FtPZjAOuxWOfEoav6sT4mptAOnjzmiVLO2tazNpDw==","signedAt":"2026-06-21T22:58:46.784Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/npm-modelcontextprotocolserver-system-monitor","artifact":"https://unfragile.ai/npm-modelcontextprotocolserver-system-monitor","verify":"https://unfragile.ai/api/v1/verify?slug=npm-modelcontextprotocolserver-system-monitor","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}