Expert system hardware probe and performance diagnostic engine for AI, Gaming, and High-Performance workflows. Provides deep system insights such as real-time monitoring, thermal diagnostics, and LLM optimization
Installation and connection instructions are shown only when supported by retained package, repository, or endpoint evidence.
Version 1.1.3 declares 1 executable entrypoint.
npm install --save-exact @yamaru-eu/hardware-probe@1.1.3npx -y -p @yamaru-eu/hardware-probe@1.1.3 @yamaru-eu/hardware-probe{
"mcpServers": {
"@yamaru-eu/hardware-probe": {
"command": "npx",
"args": [
"-y",
"-p",
"@yamaru-eu/hardware-probe@1.1.3",
"@yamaru-eu/hardware-probe"
]
}
}
}| Canonical slug | yamaru-hardware-probe-cbbbb723 | Deployment | Local Only |
|---|---|---|---|
| Canonical package | npm:@yamaru-eu/hardware-probe | Repository | yamaru-eu/hardware-probe |
| First published | — | Latest release | — |
| Last security verification | Aug 20, 2026 | Classification confidence | 90% |
| Publication | Published | Official distribution | Yes |
| Channel | Identifier | Current version | Versions | Source |
|---|---|---|---|---|
| npm | @yamaru-eu/hardware-probe | 1.1.3 | 1 | Repository |
| Package | Version | Published / observed | Inventory | Security scan |
|---|---|---|---|---|
| npm@yamaru-eu/hardware-probe | 1.1.3Current | Sep 5, 2026 | 11 toolsSucceeded · 0 resources · 0 prompts | Verified clean |
Independently scan the exact version your agents use, receive alerts when its risk changes, and investigate every finding with retained version evidence.
| Provenance | artifact_hash_verified | Signature | — |
|---|---|---|---|
| MCP SDK | @modelcontextprotocol/sdk | Artifact SHA-256 | e3fed0bac476c7879b3d3b2c72445a3ecfdceccd42be1cbd323ef7ce60810bbe |
| Scanner | mcp-proof-engine 0.1.0 | Scan completed | Aug 20, 2026 |
| Security rating | 37 / 100 | Methodology | version-rating-1.0 |
[ "@yamaru-eu/hardware-probe" ]
[ "security_policy_not_observed" ]
| Tool | Category | Annotations | Risk |
|---|---|---|---|
analyze_inference_configPerforms a deep scan for LLM inference optimization: GPU details, real memory bandwidth benchmark, ML runtimes (Ollama, Docker, WSL), and environment variables.Input schema{
"type": "object",
"properties": {},
"required": []
} | — | — | |
analyze_local_systemReads the hardware specifications of the local machine: CPU, RAM, motherboard and OS. Returns a structured JSON object.Input schema{
"type": "object",
"properties": {},
"required": []
} | — | — | |
analyze_performanceProvides real-time system performance: CPU load, memory usage, and top processes.Input schema{
"type": "object",
"properties": {},
"required": []
} | — | — | |
analyze_ram_pressureReports current memory pressure: total/used/free/available memory, swap usage, and top processes by RSS.Input schema{
"type": "object",
"properties": {},
"required": []
} | — | — | |
check_llm_compatibility(BETA) Checks if a specific LLM model can run on this machine. Returns optimal quantization and estimated tokens per second. Requires remote API connection.Input schema{
"type": "object",
"properties": {
"model_name": {
"type": "string",
"description": "LLM model name (e.g., 'Llama 3.1 70B')"
}
},
"required": [
"model_name"
]
} | — | — | |
check_storage_healthReports per-disk health: type (NVMe/SSD/HDD), vendor, temperature, SMART status, and firmware.Input schema{
"type": "object",
"properties": {},
"required": []
} | — | — | |
diagnose_antivirus_impactDetects running antivirus/EDR products and reads their exclusion rules. Checks dev hot paths coverage.Input schema{
"type": "object",
"properties": {},
"required": []
} | — | — | |
get_inference_expert_knowledgeReturns the expert instructions and rules for optimizing LLM inference on this specific hardware. Call this to learn HOW to interpret analyze_inference_config results.Input schema{
"type": "object",
"properties": {},
"required": []
} | — | — | |
get_llm_recommendations(BETA) Recommends the best LLM models that can run locally on this machine. Requires remote API connection.Input schema{
"type": "object",
"properties": {
"use_case": {
"type": "string",
"enum": [
"general",
"coding",
"reasoning",
"chat"
],
"description": "Use case"
},
"limit": {
"type": "number",
"description": "Max recommendations"
}
}
} | — | — | |
monitor_system_healthMonitors system metrics (CPU load, RAM usage, CPU temperature) over a configurable duration (up to 10 minutes) and returns min/max/avg statistics. Use duration_seconds to set the observation window and interval_seconds to control sampling granularity.Input schema{
"type": "object",
"properties": {
"duration_seconds": {
"type": "number",
"description": "Monitoring duration in seconds (1-600)",
"default": 10
},
"interval_seconds": {
"type": "number",
"description": "Sampling interval in seconds",
"default": 2
}
}
} | — | — | |
thermal_profileReports current CPU and GPU thermal and frequency state: temperature, utilization, and fan speed.Input schema{
"type": "object",
"properties": {},
"required": []
} | — | — |
| Endpoint | Transport | Authentication | Health | Observed |
|---|---|---|---|---|
| No verified remote endpoint is linked. | ||||
Install the selected package version with: npm install --save-exact @yamaru-eu/hardware-probe@1.1.3
Yamaru Hardware Probe MCP Server exposed 11 tools during independent protocol observation, including analyze_inference_config, analyze_local_system, analyze_performance, analyze_ram_pressure, check_llm_compatibility, check_storage_health, diagnose_antivirus_impact, get_inference_expert_knowledge, and others.
Our scanner tested version 1.1.3 without proving a finding in the methods exercised. This is not a guarantee that every deployment is secure.
Curated product and capability guides containing this catalog record.