Yamaru Hardware Probe
OfficialServer Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| analyze_local_systemA | Reads the hardware specifications of the local machine: CPU, RAM, motherboard and OS. Returns a structured JSON object. |
| analyze_performanceB | Provides real-time system performance: CPU load, memory usage, and top processes. |
| analyze_inference_configA | Performs a deep scan for LLM inference optimization: GPU details, real memory bandwidth benchmark, ML runtimes (Ollama, Docker, WSL), and environment variables. |
| get_inference_expert_knowledgeA | Returns the expert instructions and rules for optimizing LLM inference on this specific hardware. Call this to learn HOW to interpret analyze_inference_config results. |
| check_llm_compatibilityA | (BETA) Checks if a specific LLM model can run on this machine. Returns optimal quantization and estimated tokens per second. Requires remote API connection. |
| get_llm_recommendationsB | (BETA) Recommends the best LLM models that can run locally on this machine. Requires remote API connection. |
| analyze_ram_pressureA | Reports current memory pressure: total/used/free/available memory, swap usage, and top processes by RSS. |
| check_storage_healthA | Reports per-disk health: type (NVMe/SSD/HDD), vendor, temperature, SMART status, and firmware. |
| thermal_profileA | Reports current CPU and GPU thermal and frequency state: temperature, utilization, and fan speed. |
| diagnose_antivirus_impactA | Detects running antivirus/EDR products and reads their exclusion rules. Checks dev hot paths coverage. |
| monitor_system_healthA | Monitors 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. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 11 tools
Several tools overlap in purpose: analyze_ram_pressure, analyze_performance, and monitor_system_health all deal with CPU/memory metrics, causing potential confusion. Additionally, system specs are split across analyze_local_system and analyze_inference_config, adding ambiguity.
Tool names mostly follow a verb_noun pattern but use a mix of verbs (analyze, check, get, diagnose, monitor) without uniform style. Some compound nouns are inconsistent in format, making the set moderately predictable.
With 11 tools, the count is well within the 3-15 optimal range. Each tool serves a distinct purpose related to hardware probing and LLM inference, without being excessive.
The set covers hardware specs, storage health, thermal, performance, and LLM inference utilities, but lacks basic metrics like disk usage and network info. The inclusion of antivirus impact is niche, while GPU info is only available in inference context.