Infra Lens

Explain Linux incidents over SSH with baseline-aware MCP tooling.

npm version npm downloads License: MIT Node 20+ MCP SDK Azure Pipelines

Demo

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."
    }
  ]
}

What It Does

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.

• Collects CPU, memory, disk, network, process, and OS data without mutating the target host
• Records local metric history in SQLite for baselines, comparisons, and trend lookups
• Uses z-score analysis for CPU anomaly detection once enough baseline samples exist
• Explains the likely cause of pressure, not just the raw metric value
• Supports MCP over stdio and Streamable HTTP

How It Works

flowchart 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.

Tools

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?

Quick Start

1. Run via npx

npx -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

2. Claude Desktop

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"
      }
    }
  }
}

3. Docker

docker build -t mcp-infra-lens .
docker run --rm -it \
  -v "$HOME/.mcp-infra-lens:/home/appuser/.mcp-infra-lens" \
  mcp-infra-lens

Configuration

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

Health Score

• 90-100: healthy, no meaningful anomalies detected
• 70-89: mild or isolated pressure
• 40-69: multiple warnings or a major issue in progress
• 0-39: critical condition with urgent remediation needed

Recommended Workflow

1. Record record_baseline samples during healthy operating windows.
2. Use analyze_server during incidents or load spikes.
3. Use compare_to_baseline for a tighter differential view against a named baseline.
4. Use get_history to inspect trends and separate default snapshots from labeled baseline sessions.

Authentication

The SSH input schema supports:

• Password authentication
• Inline private key authentication
• Optional passphrase support for encrypted keys

Credential fields are redacted from structured logs before they are written to stderr.

Security Notes

• SSH collection is read-only on the target host
• SSH credentials are never stored in SQLite
• Host key verification is permissive in v1 for compatibility; production deployments should restrict outbound network access and plan to enforce strict host verification in a later release
• The HTTP transport has no built-in authentication; bind to loopback and place it behind an authenticated reverse proxy in any non-local deployment

See SECURITY.md for the reporting policy and stored-data scope.

Integrations

Claude Desktop

{
  "mcpServers": {
    "infra-lens": {
      "command": "npx",
      "args": ["-y", "mcp-infra-lens"],
      "env": {
        "INFRA_LENS_DB": "/Users/you/.mcp-infra-lens/metrics.db"
      }
    }
  }
}

Cursor IDE

{
  "mcpServers": {
    "infra-lens": {
      "command": "npx",
      "args": ["-y", "mcp-infra-lens"]
    }
  }
}

VS Code (MCP extension)

{
  "inputs": [],
  "servers": {
    "infra-lens": {
      "type": "stdio",
      "command": "npx",
      "args": ["-y", "mcp-infra-lens"]
    }
  }
}

Windsurf

{
  "mcpServers": {
    "infra-lens": {
      "command": "npx",
      "args": ["-y", "mcp-infra-lens"]
    }
  }
}

Docker (HTTP transport)

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.

Docker

The bundled Docker image:

• Builds the TypeScript project in a separate stage
• Rebuilds better-sqlite3 for the container architecture in both stages
• Runs as a non-root appuser
• Stores SQLite data in /home/appuser/.mcp-infra-lens/metrics.db

Contributing

Contributions are welcome. Start with CONTRIBUTING.md, then use:

• docs/usage.md for tool examples
• docs/architecture.md for the component map
• docs/testing.md for local validation and publish checks
• RELEASE_POLICY.md for npm and MCP Registry versioning rules
• AGENTS.md, CLAUDE.md, GEMINI.md, .github/copilot-instructions.md, and .agent/rules/repository.md for repository-specific AI coding guidance

Operational / CI Notes

• azure-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 workflows
• Publish only after the local pre-publish checklist and CI both pass cleanly on Node 20
• Follow RELEASE_POLICY.md when deciding whether a change needs npm, MCP Registry, or registry-only prerelease publication

License

MIT