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Chandukasireddy

system-mcp

README.md
# system-mcp

A local MCP server (Python + `uv`) that exposes system telemetry tools:

- CPU usage and core/frequency metrics
- RAM and swap usage
- Disk usage and IO
- Battery status
- Network counters
- Running task/process stats
- GPU metrics (via `nvidia-smi` when available)

## 1) Install

```bash
uv sync
```

## 2) Run server (stdio transport)

```bash
uv run system-mcp
```

## 3) MCP client configuration example

Use this command in your MCP client config:

- Command: `uv`
- Args: `run`, `system-mcp`
- Working directory: this project root

Example JSON snippet:

```json
{
  "mcpServers": {
    "system-mcp": {
      "command": "uv",
      "args": ["run", "system-mcp"],
      "cwd": "k:/Tech/Projects/system_mcp"
    }
  }
}
```

## Exposed tools

- `system_overview`
- `cpu_metrics(sample_seconds=1.0)`
- `memory_metrics`
- `disk_metrics(path="C:\\")`
- `battery_metrics`
- `network_metrics`
- `running_tasks(limit=10, sort_by="cpu")`
- `gpu_metrics`

## Notes

- `gpu_metrics` depends on `nvidia-smi` being available in `PATH`.
- On systems without battery/GPU, tools return availability flags and messages instead of failing.

TDQS

A3.7/5.0

Scored across 8 tools

Disambiguation5/5

Each tool targets a distinct system component (battery, CPU, disk, GPU, memory, network, processes, and overview) with no functional overlap.

Naming Consistency5/5

All tool names follow a consistent snake_case pattern with descriptive suffixes like _metrics, _tasks, _overview.

Tool Count5/5

8 tools is well-scoped for a system monitoring server, covering major resources without being excessive or too sparse.

Completeness4/5

Core system metrics are covered, but lacks deeper details like per-interface network stats or sensor data, which are minor gaps.

Maintenance

ActivityInactive
ResponsivenessNo issues