silentwatch-mcp

MCP server for catching cron silent failures — when scheduled jobs exit 0 with empty output, when retry storms run away, when action budgets leak. Surfaces overdue jobs, length anomalies, and silent-fail patterns to any Claude or MCP-aware agent. Works with system cron, systemd timers, OpenClaw cron logs, and any JSONL run-log out of the box. Keywords: AI agent monitoring, cron health, scheduled-task observability, production AI ops.

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What it does

Real silent failures from production AI deployments in the last 30 days:

• GitHub Issue #54260, anthropics/claude-code — Claude Code Routines: cron triggers fire and the routine state advances (ended_reason: run_once_fired), but the cloud container never reaches prompt execution. This silently affected the operator's routines for at least 28 days before they noticed the output files weren't updating.
• GitHub Issue #1243, anthropics/claude-code-action — claude-sonnet-4-6 returns empty assistant turns in a tight loop (stop_reason: null, output_tokens: 8) for ~20 minutes. The workflow step then exits as success with no artifacts produced — the GitHub Actions API can't distinguish "completed cleanly" from "returned empty for 20 minutes burning Claude Max budget."
• dev.to: "5 Silent Failure Patterns I Keep Finding in Production AI Systems" — the systematic taxonomy.

These all map to one underlying problem: exit-code monitoring lies. The job returned 0; the data is broken anyway. Any team running scheduled jobs has hit at least one of these:

• Silent failure — the job ran, returned exit code 0, but produced no useful output (a web-search cron returning empty, a backup that wrote a 0-byte file, a digest email that sent with <no rows> in the body). Traditional monitoring sees a green checkmark; the data is broken anyway.
• Overdue without alert — a job stopped running for 3 days; nobody noticed because nobody was watching
• Last-success drift — the job runs every hour but only succeeded once in the last 12 attempts; everyone assumes it's healthy because the most recent run was green
• Audit-trail gap — you need to know when a specific job last completed for a compliance check, and the only "log" is journalctl output that rotated last week

silentwatch-mcp exposes that visibility as MCP tools your AI agent can query directly. No metrics pipeline, no separate dashboard, no SaaS subscription.

> claude: which of my cron jobs have silent failures in the last 24 hours?
[MCP tool: find_silent_failures]
3 jobs flagged:
  • web-search-refresh — ran 12× successfully but output empty in 8 (66% silent fail rate)
  • daily-summary — ran 1× successfully (24× expected); output normal
  • audit-snapshot — last success 5 days ago, all subsequent runs returned exit 0 with empty body

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Why silentwatch-mcp

Three things existing tools (Cronitor, Healthchecks.io, Datadog, Prometheus) don't do:

1. Detect silent failures, not just exit codes. Traditional cron monitoring assumes exit 0 = success. We check the output against configurable rules: empty output, length anomaly vs historical median, error keywords in stdout despite exit 0, duration anomaly. The job that "ran successfully" but returned nothing useful — that's the failure mode that hides for weeks. We catch it.
2. MCP-native, no integration layer. Claude Desktop, Cline, Continue, OpenClaw agents — any MCP-aware client queries directly. No Grafana plugin, no API wrapper, no JSON to parse manually.
3. Multi-source out of the box. OpenClaw native JSONL logs, system crontab (/etc/crontab + /etc/cron.d/* + per-user crontab -l), and systemd timers (systemctl list-timers + journalctl) — all four backends ship in v0.3, so you can run silentwatch-mcp against whatever scheduler you have. No vendor lock-in.

Built for the SMB self-hoster running a $40 VPS where Datadog is overkill and a "$0/mo open-source MCP" is the right price point — but the silent-failure detection is just as valuable on enterprise infra.

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Tool surface

The server registers these MCP tools (full spec in SPEC.md):

Tool	What it does
list_jobs	Enumerate all known cron jobs with last-run summary
get_job_status(job_id)	Detailed status for one job: last run, last success, success rate over window
get_job_runs(job_id, limit)	Recent run history with timing + status + output snippet
find_overdue_jobs	Jobs whose schedule says they should have run but haven't
find_silent_failures(window_hours)	Jobs that ran "successfully" but output looks suspicious
tail_job_logs(job_id, lines)	Recent log output for one job

Resources:

• cron://jobs — list of all jobs (manifest)
• cron://job/{id} — individual job manifest + recent runs
• cron://run/{id} — individual run instance with full output

Prompts:

• diagnose-overdue — diagnostic prompt template for an overdue job
• summarize-cron-health — daily digest of cron activity + anomalies

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Quickstart

v0.3 beta — all 4 backends shipped + real overdue detection via cron-schedule parsing (croniter). Mock, OpenClaw JSONL, crontab, and systemd backends are all production-ready. 74 tests passing. v1.0 is now polish: PyPI release + GitHub Actions CI + MCP registry submissions.

Install

pip install silentwatch-mcp  # not yet on PyPI; install from source for now:
pip install -e .

Configure for Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

{
  "mcpServers": {
    "silentwatch": {
      "command": "python",
      "args": ["-m", "silentwatch_mcp"],
      "env": {
        "SILENTWATCH_BACKEND": "mock"
      }
    }
  }
}

Backends (all four shipped as of v0.3):

• SILENTWATCH_BACKEND=mock — returns sample data (default for development)
• SILENTWATCH_BACKEND=openclaw-jsonl — parses OpenClaw's native cron run JSONL files (set SILENTWATCH_OPENCLAW_LOGS to the directory, default ~/.openclaw/cron-runs/); richest data — full run history + silent-fail detection
• SILENTWATCH_BACKEND=crontab — parses /etc/crontab + /etc/cron.d/* + user crontabs (crontab -l); last-run inferred from /var/log/syslog or /var/log/cron (set SILENTWATCH_SYSLOG to override)
• SILENTWATCH_BACKEND=systemd — parses systemctl list-timers --all --output=json + journalctl -u <unit> for run history; lifts OnCalendar= into the schedule field

All non-mock backends gracefully return empty results on platforms / hosts where the underlying tooling isn't present, so configuration is safe to leave in place across environments.

Restart Claude Desktop

The server registers as silentwatch. Test:

Show me all my cron jobs and their last-run status.

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Roadmap

Version	Scope	Status
v0.1	Protocol wiring, mock backend, all 6 tools registered with stub data, tests pass	✅ Complete
v0.2	OpenClaw JSONL backend implemented (real cron run parsing, malformed-line handling, silent-fail enrichment)	✅ Complete (2026-05-02)
v0.3	Crontab + systemd backends; cron-schedule parsing for real overdue detection (croniter); 35 new tests	✅ Complete (2026-05-02)
v1.0	Polish: PyPI release, GitHub Actions CI, MCP registry submissions (Glama + PulseMCP), refined silent-fail rule configuration	⏳ Phase 1 ship target (W3, May 18)
v1.x	Additional backends (Cowork scheduler, Claude Code background tasks, generic JSON config), webhook emitter for alerts	⏳ Phase 2+

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Need this adapted to your stack?

silentwatch-mcp ships with 4 backends (mock, OpenClaw JSONL, crontab, systemd). If your scheduler is something else — AWS EventBridge, GCP Cloud Scheduler, Hangfire, Sidekiq, Temporal, Apache Airflow, Prefect, Dagster, or a custom job runner — and you want the same silent-failure-detection MCP visibility surface for it, that's a Custom MCP Build engagement.

Tier	Scope	Investment	Timeline
Simple	Single backend adapter for an existing scheduler with documented API (e.g., GCP Cloud Scheduler)	$8,000–$10,000	1–2 weeks
Standard	Custom backend + custom silent-fail rules + integration with your existing alerting (PagerDuty, Slack, etc.)	$15,000–$20,000	2–4 weeks
Complex	Multi-backend (federated cron across regions / clusters / tenants) + RBAC + audit-log integration + on-call workflow	$25,000–$35,000	4–8 weeks

To engage:

1. Email temur@pixelette.tech with subject Custom MCP Build inquiry
2. Include: a 1-paragraph description of your scheduler stack + which tier you're considering
3. Reply within 2 business days with a 30-min discovery call slot

This server is also part of the AI Production Discipline Framework — the methodology underlying production AI audits I run.

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Production AI audits

If you're running production AI and want an outside practitioner to score readiness, find the failure patterns that are already present, and write the corrective-action plan — that's what this MCP is built into supporting. The standalone audit service:

Tier	Scope	Investment	Timeline
Audit Lite	One system, top-5 findings, written report	$1,500	1 week
Audit Standard	Full audit, all 14 patterns, 5 Cs findings, 90-day follow-up	$3,000	2–3 weeks
Audit + Workshop	Standard audit + 2-day team workshop + first monthly audit included	$7,500	3–4 weeks

Same email channel: temur@pixelette.tech with subject AI audit inquiry.

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Contributing

PRs welcome. The structure is intentionally flat to make custom backends easy to add — see src/silentwatch_mcp/backends/ for existing examples.

To add a new backend:

1. Subclass CronBackend in backends/<your_backend>.py
2. Implement list_jobs, get_job_runs, tail_logs
3. Register in backends/__init__.py
4. Add tests in tests/test_backend_<your_backend>.py

Bug reports + feature requests: open a GitHub issue.

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License

MIT — see LICENSE.

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Related

• Production-AI MCP Suite (Gumroad bundle) — this server plus 6 others (openclaw-health-mcp, openclaw-cost-tracker-mcp, openclaw-skill-vetter-mcp, openclaw-upgrade-orchestrator-mcp, openclaw-output-vetter-mcp, bash-vet-mcp) in one curated 7-pack bundle with a decision tree, day-one drill, and Custom MCP Build CTA. $99, or $49 with LAUNCH50 for the first 30 days.
• openclaw-health-mcp — deployment health (gateway, CPU/RAM, skills, recent errors)
• openclaw-cost-tracker-mcp — token-cost telemetry + 429 prediction (v1.1+)
• openclaw-skill-vetter-mcp — ClawHub skill + agent-config security vetting (v1.1+)
• openclaw-upgrade-orchestrator-mcp — read-only upgrade advisor + provider-side regression detection (v1.2+)
• openclaw-output-vetter-mcp — agent claim verification (inline grounding-check + swallowed-exception scanner + multi-turn transcript review + action-outcome verifier v1.1+)
• bash-vet-mcp — pre-execution shell-command vetting (28 destructive-pattern rules across 8 families)
• AI Production Discipline Framework — Notion template, $29 — the full 14-pattern catalog this MCP server is built around
• AI Production Auditor (GPT Store) — paste your config or agent setup, get a 5 Cs audit report. Free, ChatGPT-only.
• SPEC.md — full server design
• Model Context Protocol — protocol overview

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Built by Temur Khan — independent practitioner on production AI systems. Contact: temur@pixelette.tech