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SSH Linux metrics, baselines, anomaly detection, and plain-English infrastructure explanations.
SSH Linux metrics, baselines, anomaly detection, and plain-English infrastructure explanations.
Valid MCP server (2 strong, 2 medium validity signals). No known CVEs in dependencies. Package registry verified. Imported from the Official MCP Registry.
9 files analyzed · 1 issue found
Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.
This plugin requests these system permissions. Most are normal for its category.
Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-oaslananka-mcp-infra-lens": {
"args": [
"-y",
"mcp-infra-lens"
],
"command": "npx"
}
}
}From the project's GitHub README.
Explain Linux incidents over SSH with baseline-aware MCP tooling.

Sample analyze_server response when Claude asks, "What's wrong with prod-01?":
{
"host": "prod-01.internal",
"health_score": 42,
"summary": "Found 2 anomalies on prod-01.internal. Most urgent signal: CPU is at 91% (3.4σ above baseline 28.2%). Load is 7.2/6.8/5.1. Top CPU consumer: java (87%).",
"anomalies": [
{
"metric": "cpu",
"severity": "high",
"value": 91,
"z_score": 3.4,
"explanation": "CPU is at 91% (3.4σ above baseline 28.2%). Load is 7.2/6.8/5.1. Top CPU consumer: java (87%).",
"recommendation": "Investigate java (PID 18432) and review application logs or scale-out options."
},
{
"metric": "disk:/",
"severity": "high",
"value": 91,
"explanation": "Disk / is 91% full (182GB/200GB).",
"recommendation": "Run du -sh //* | sort -rh | head -20 and clean logs or temporary files."
}
]
}
mcp-infra-lens connects to Linux hosts over SSH, captures a live infrastructure snapshot, compares it to recently recorded baselines, and explains anomalies in plain English.
stdio and Streamable HTTPflowchart TD
A["Claude / Cursor / VS Code / Windsurf"] --> B["mcp-infra-lens"]
B --> C["server-core.ts"]
C --> D["collector.ts"]
C --> E["analyzer.ts"]
C --> F["baseline.ts + db.ts"]
D --> G["ssh.ts"]
G --> H["Linux host over SSH"]
F --> I["SQLite history + baselines"]
analyze_server now performs real sampled collection over the requested duration_minutes, averages CPU and memory pressure across the collection window, persists the resulting snapshot, and then runs anomaly detection against the selected baseline.
| Tool | What it does | Key params |
|---|---|---|
analyze_server | Collects a sampled snapshot, stores it, and explains anomalies | connection, duration_minutes, include_processes, include_network |
snapshot | Captures and stores the current point-in-time metrics without analysis | connection |
record_baseline | Saves a labeled healthy-state sample for future comparisons | connection, label |
compare_to_baseline | Compares the current state to a named baseline and explains the deltas | connection, baseline_label |
get_history | Returns historical CPU, memory, or load points from SQLite | host, metric, hours, label? |
npxnpx -y mcp-infra-lens
If you are pinned to 1.0.1, upgrade to 1.0.2 or newer to avoid Node 24 native install failures:
npx -y mcp-infra-lens@latest
Published package:
{
"mcpServers": {
"infra-lens": {
"command": "npx",
"args": ["-y", "mcp-infra-lens"],
"env": {
"INFRA_LENS_DB": "/Users/you/.mcp-infra-lens/metrics.db"
}
}
}
}
Local development:
{
"mcpServers": {
"infra-lens": {
"command": "node",
"args": ["/absolute/path/to/mcp-infra-lens/dist/mcp.js"],
"env": {
"INFRA_LENS_DB": "/Users/you/.mcp-infra-lens/metrics.db"
}
}
}
}
docker build -t mcp-infra-lens .
docker run --rm -it \
-v "$HOME/.mcp-infra-lens:/home/appuser/.mcp-infra-lens" \
mcp-infra-lens
| Environment variable | Default | Description |
|---|---|---|
INFRA_LENS_DB | ~/.mcp-infra-lens/metrics.db | SQLite database path. Use :memory: for tests |
HOST | 127.0.0.1 | Bind address for the HTTP transport |
PORT | 3000 | Port for the HTTP transport |
90-100: healthy, no meaningful anomalies detected70-89: mild or isolated pressure40-69: multiple warnings or a major issue in progress0-39: critical condition with urgent remediation neededrecord_baseline samples during healthy operating windows.analyze_server during incidents or load spikes.compare_to_baseline for a tighter differential view against a named baseline.get_history to inspect trends and separate default snapshots from labeled baseline sessions.The SSH input schema supports:
Credential fields are redacted from structured logs before they are written to stderr.
See SECURITY.md for the reporting policy and stored-data scope.
{
"mcpServers": {
"infra-lens": {
"command": "npx",
"args": ["-y", "mcp-infra-lens"],
"env": {
"INFRA_LENS_DB": "/Users/you/.mcp-infra-lens/metrics.db"
}
}
}
}
{
"mcpServers": {
"infra-lens": {
"command": "npx",
"args": ["-y", "mcp-infra-lens"]
}
}
}
{
"inputs": [],
"servers": {
"infra-lens": {
"type": "stdio",
"command": "npx",
"args": ["-y", "mcp-infra-lens"]
}
}
}
{
"mcpServers": {
"infra-lens": {
"command": "npx",
"args": ["-y", "mcp-infra-lens"]
}
}
}
docker run -d \
-p 3000:3000 \
-v $HOME/.mcp-infra-lens:/home/appuser/.mcp-infra-lens \
ghcr.io/oaslananka/mcp-infra-lens:latest
Then configure your MCP client to use http://localhost:3000.
The bundled Docker image:
better-sqlite3 for the container architecture in both stagesappuser/home/appuser/.mcp-infra-lens/metrics.dbContributions are welcome. Start with CONTRIBUTING.md, then use:
AGENTS.md, CLAUDE.md, GEMINI.md, .github/copilot-instructions.md, and .agent/rules/repository.md for repository-specific AI coding guidanceazure-pipelines.yml is the canonical CI pipeline and now runs a Quality stage on Node 20 and Node 22, publishes JUnit and Cobertura artifacts, and executes Docker-backed SSH e2e coverage on Node 20.azure/pipelines/publish.yml remains the manual npm release pipeline.azure/pipelines/mirror.yml remains available for repository mirroring workflowsBe the first to review this server!
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