監視ツールが検知できないcronの失敗をキャッチします。 スケジュール済みジョブの状態（実行状況、期限切れジョブ、終了コード0だが有用な出力がないサイレント障害）をClaudeやMCP対応エージェントに提供するMCPサーバーです。OpenClawスケジューラー、システムcron、systemdタイマーでそのまま動作します。
スケジュール済みジョブを運用するチームなら、少なくとも一度は以下のような経験があるはずです：
サイレント障害 — ジョブは実行され、終了コード0を返したが、有用な出力がなかった（Web検索cronが空の結果を返す、バックアップが0バイトのファイルを書き込む、本文に <no rows> と書かれたダイジェストメールが送信されるなど）。従来の監視ツールでは「正常」と判定されますが、データは壊れたままです。
アラートなしの期限切れ — ジョブが3日間停止していたが、誰も監視していなかったため気づかなかった。
最終成功のドリフト — 1時間ごとに実行されるジョブが、過去12回の試行のうち1回しか成功していない。直近の実行が正常だったため、全員が健全だと誤認している。
監査証跡の欠落 — コンプライアンスチェックのために特定のジョブが最後に完了した日時を知る必要があるが、唯一の「ログ」である journalctl の出力が先週ローテーションされて消えてしまった。
silentwatch-mcp は、この可視性をAIエージェントが直接クエリできるMCPツールとして公開します。メトリクスパイプラインも、個別のダッシュボードも、SaaSのサブスクリプションも不要です。
> 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 bodyRelated MCP server: task-orchestrator
silentwatch-mcp を選ぶ理由既存のツール（Cronitor、Healthchecks.io、Datadog、Prometheus）が対応していない3つの機能を提供します：
終了コードだけでなく、サイレント障害を検出。 従来のcron監視は 終了コード0 = 成功 と見なします。私たちは設定可能なルールに基づいて 出力 をチェックします：空の出力、過去の中央値と比較した長さの異常、終了コード0にもかかわらずstdoutに含まれるエラーキーワード、実行時間の異常など。「正常に実行された」はずなのに有用な結果を返さないジョブ、これこそが数週間も隠れ続ける障害モードです。私たちはこれをキャッチします。
MCPネイティブ、統合レイヤー不要。 Claude Desktop、Cline、Continue、OpenClawエージェントなど、MCP対応クライアントであれば直接クエリ可能です。Grafanaプラグインも、APIラッパーも、手動で解析するJSONも不要です。
標準でマルチソース対応。 OpenClawネイティブのJSONLログ、システムcrontab (/etc/crontab + /etc/cron.d/* + ユーザーごとの crontab -l)、systemdタイマー (systemctl list-timers + journalctl) — これら4つのバックエンドがv0.3から同梱されているため、お使いのスケジューラーに関わらず silentwatch-mcp を実行できます。ベンダーロックインはありません。
Datadogでは過剰で、「月額0ドルのオープンソースMCP」が適正価格であるような、40ドルのVPSで運用するSMBセルフホストユーザー向けに構築されています。もちろん、エンタープライズ環境でもサイレント障害検出の価値は同様です。
サーバーは以下のMCPツールを登録します（詳細は SPEC.md を参照）：
ツール | 機能 |
| 既知のすべてのcronジョブと最終実行サマリーを列挙 |
| 特定ジョブの詳細ステータス：最終実行、最終成功、期間内の成功率 |
| タイミング、ステータス、出力スニペットを含む最近の実行履歴 |
| スケジュール上は実行されるべきだが実行されていないジョブ |
| 「正常」に実行されたが、出力が疑わしいジョブ |
| 特定ジョブの最近のログ出力 |
リソース：
cron://jobs — すべてのジョブのリスト（マニフェスト）
cron://job/{id} — 個別のジョブマニフェスト + 最近の実行履歴
cron://run/{id} — 完全な出力を含む個別の実行インスタンス
プロンプト：
diagnose-overdue — 期限切れジョブの診断プロンプトテンプレート
summarize-cron-health — cronアクティビティと異常のデイリーダイジェスト
v0.3 beta — 4つのバックエンドすべてを同梱 + cron-schedule解析 (croniter) によるリアルな期限切れ検出。 Mock、OpenClaw JSONL、crontab、systemdバックエンドはすべて本番環境対応済みです。74のテストを通過。v1.0に向けて仕上げの段階です：PyPIリリース、GitHub Actions CI、MCPレジストリへの登録。
pip install silentwatch-mcp # not yet on PyPI; install from source for now:
pip install -e .~/Library/Application Support/Claude/claude_desktop_config.json (macOS) または %APPDATA%\Claude\claude_desktop_config.json (Windows) に追加してください：
{
"mcpServers": {
"silentwatch": {
"command": "python",
"args": ["-m", "silentwatch_mcp"],
"env": {
"SILENTWATCH_BACKEND": "mock"
}
}
}
}バックエンド（v0.3時点で4つすべて同梱）：
SILENTWATCH_BACKEND=mock — サンプルデータを返します（開発時のデフォルト）
SILENTWATCH_BACKEND=openclaw-jsonl — OpenClawのネイティブcron実行JSONLファイルを解析します（SILENTWATCH_OPENCLAW_LOGS にディレクトリを指定、デフォルトは ~/.openclaw/cron-runs/）。最もリッチなデータ（完全な実行履歴 + サイレント障害検出）が得られます。
SILENTWATCH_BACKEND=crontab — /etc/crontab + /etc/cron.d/* + ユーザーcrontab (crontab -l) を解析します。最終実行は /var/log/syslog または /var/log/cron から推測されます（SILENTWATCH_SYSLOG で上書き可能）。
SILENTWATCH_BACKEND=systemd — systemctl list-timers --all --output=json + journalctl -u <unit> を解析して実行履歴を取得します。OnCalendar= をスケジュールフィールドに反映します。
Mock以外のバックエンドは、基盤となるツールが存在しないプラットフォームやホストでは空の結果を正常に返すため、環境をまたいで設定をそのままにしておいても安全です。
サーバーは silentwatch として登録されます。テスト：
Show me all my cron jobs and their last-run status.
バージョン | スコープ | ステータス |
v0.1 | プロトコル接続、mockバックエンド、スタブデータで6つのツールを登録、テスト通過 | ✅ 完了 |
v0.2 | OpenClaw JSONLバックエンドの実装（実際のcron実行解析、不正な行の処理、サイレント障害の強化） | ✅ 完了 (2026-05-02) |
v0.3 | Crontab + systemdバックエンド、cron-schedule解析によるリアルな期限切れ検出 (croniter)、35の新規テスト | ✅ 完了 (2026-05-02) |
v1.0 | 仕上げ：PyPIリリース、GitHub Actions CI、MCPレジストリへの登録 (Glama + PulseMCP)、サイレント障害ルール設定の洗練 | ⏳ フェーズ1出荷目標 (5月18日の週) |
v1.x | 追加バックエンド（Coworkスケジューラー、Claude Codeバックグラウンドタスク、汎用JSON設定）、アラート用Webhookエミッター | ⏳ フェーズ2以降 |
silentwatch-mcp は4つのバックエンド（mock、OpenClaw JSONL、crontab、systemd）を同梱しています。もしお使いのスケジューラーがAWS EventBridge、GCP Cloud Scheduler、Hangfire、Sidekiq、Temporal、Apache Airflow、Prefect、Dagster、あるいはカスタムジョブランナーなどであり、同様のサイレント障害検出MCP可視化が必要な場合は、カスタムMCP構築のエンゲージメントをご検討ください。
ティア | スコープ | 投資額 | 期間 |
シンプル | APIがドキュメント化されている既存スケジューラー（例：GCP Cloud Scheduler）用の単一バックエンドアダプター | $8,000–$10,000 | 1–2週間 |
標準 | カスタムバックエンド + カスタムサイレント障害ルール + 既存のアラート（PagerDuty、Slackなど）との統合 | $15,000–$20,000 | 2–4週間 |
複雑 | マルチバックエンド（リージョン/クラスター/テナントをまたぐフェデレーションcron） + RBAC + 監査ログ統合 + オンコールワークフロー | $25,000–$35,000 | 4–8週間 |
依頼方法：
件名を Custom MCP Build inquiry として admin@pixelette.tech までメールしてください。
スケジューラースタックの概要（1段落）と検討中のティアを記載してください。
2営業日以内に30分間のディスカバリーコールの日程を返信します。
このサーバーは、私が実施する本番AI監査の基礎となる手法である AI Production Discipline Framework の一部でもあります。
本番環境でAIを運用しており、外部の専門家に準備状況のスコアリング、既存の障害パターンの特定、是正措置計画の策定を依頼したい場合、このMCPがそのサポートに役立ちます。スタンドアロンの監査サービス：
ティア | スコープ | 投資額 | 期間 |
監査ライト | 1システム、上位5つの発見事項、報告書作成 | $1,500 | 1週間 |
監査標準 | 完全監査、全14パターン、5つのCの発見事項、90日間のフォローアップ | $3,000 | 2–3週間 |
監査 + ワークショップ | 標準監査 + 2日間のチームワークショップ + 初回の月次監査を含む | $7,500 | 3–4週間 |
同じメールアドレス：admin@pixelette.tech（件名：AI audit inquiry）までご連絡ください。
プルリクエストを歓迎します。カスタムバックエンドを容易に追加できるよう、構造は意図的にフラットにしています。既存の例については src/silentwatch_mcp/backends/ を参照してください。
新しいバックエンドを追加するには：
backends/<your_backend>.py で CronBackend をサブクラス化する
list_jobs、get_job_runs、tail_logs を実装する
backends/__init__.py に登録する
tests/test_backend_<your_backend>.py にテストを追加する
バグ報告や機能リクエストはGitHubのIssueで受け付けています。
MIT — LICENSE を参照してください。
AI Production Discipline Framework — Notionテンプレート、$29
SPEC.md — サーバー設計詳細
Model Context Protocol — プロトコル概要
構築者：Temur Khan — 本番AIシステムの独立系専門家。 連絡先：admin@pixelette.tech
Returns jobs whose schedule indicates they should have run but haven't, beyond a grace window.
| Name | Required | Description | Default |
|---|---|---|---|
| grace_minutes | No | Tolerance to avoid flagging jobs about to run (default 5) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses the core behavior (returns overdue jobs) but does not mention whether the operation is read-only, performance implications, or pagination. Adequate but not thorough.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single sentence that is efficient and front-loaded with the core purpose, no redundant words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one optional parameter and no output schema, the description is largely complete. It could clarify what 'schedule indicates' means or the output format, but overall it provides sufficient context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema coverage, the description adds value by explaining 'beyond a grace window' which directly connects to the grace_minutes parameter, providing context beyond the schema's technical description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns overdue jobs with a grace window, distinguishing it from siblings like find_silent_failures (different failure mode) and list_jobs (all jobs).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for checking missed jobs but lacks explicit when-to-use or when-not-to-use guidance, nor does it mention alternatives among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Jobs that returned exit code 0 but output was flagged by silent-fail rules (empty output, length anomaly, error keywords, duration anomaly).
| Name | Required | Description | Default |
|---|---|---|---|
| window_hours | No | Lookback window in hours (default 24) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; description only lists detection criteria. It does not disclose behavioral traits like read-only nature, prerequisites, or potential side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence efficiently conveys purpose but lists multiple anomaly types in a somewhat dense manner. No wasted words, but readability could improve.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given one optional parameter and no output schema, the description lacks details on return format, pagination, or usage context. Incomplete for a search/filter tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with clear description for window_hours. The tool description adds no extra meaning beyond the schema, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it finds jobs with exit code 0 flagged by silent-fail rules, using specific verb and resource. However, it does not differentiate from siblings like find_overdue_jobs.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool vs alternatives. The description implies usage for detecting silent failures but lacks when-not or alternative tool references.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent run history for a job (newest first) with timing, exit code, status, silent-fail indicators, output snippet.
| Name | Required | Description | Default |
|---|---|---|---|
| job_id | Yes | Job identifier | |
| limit | No | Max runs to return (default 20, max 500) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description carries the full burden. It discloses ordering, data fields, and indicators (e.g., silent-fail), but does not mention side effects, rate limits, access requirements, or return format specifics. This is adequate but not thorough for a tool with no annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence with all key information front-loaded (recent, newest first, data fields). No wasted words; every part adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema exists, so description should explain return structure. It lists fields but does not specify if results are an array, pagination behavior (beyond limit), or error handling. Adequate for a simple list but incomplete for comprehensive understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description does not add meaningful information beyond the schema: 'job_id' and 'limit' are already described in the schema with default and max values. No additional context for parameter usage is provided.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly specifies the resource ('run history for a job'), action ('get'), ordering ('newest first'), and included data fields ('timing, exit code, status, silent-fail indicators, output snippet'). It effectively distinguishes from sibling tools like get_job_status or tail_job_logs.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives like get_job_status or find_silent_failures. The description implies usage for recent runs but lacks when-not-to-use conditions or comparisons.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Detailed status for one job: last run, last success, success rates over 24h + 7d, overdue state, silent-fail indicators on the last run.
| Name | Required | Description | Default |
|---|---|---|---|
| job_id | Yes | Job identifier from list_jobs |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; the description describes return fields but does not disclose any behavioral traits such as read-only nature, authentication needs, or cost. For a read-like tool, this is a notable gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The entire description is a single concise sentence that front-loads the core purpose and lists specific details without any wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (one parameter, no output schema), the description adequately covers return values. It could mention error handling or prerequisites, but is largely complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema has 100% description coverage with 'job_id' documented. The description adds no further parameter information, so a baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Detailed status for one job' and enumerates specific fields (last run, success rates, overdue state, silent-fail indicators), distinguishing it from siblings like find_overdue_jobs or get_job_runs.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for obtaining a comprehensive snapshot of a single job's health, but does not explicitly state when to use this tool over alternatives or provide exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Enumerate all known cron jobs with last-run summary. Returns id, name, schedule, last run time + status, runs/successes in last 24h, silent-fail count, overdue flag.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Despite lacking annotations, the description transparently lists all returned fields, including last-run summary and overdue flags. This adequately discloses the read-only behavior and output structure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that efficiently conveys the tool's purpose and output. No extraneous information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (no parameters, no output schema), the description covers its functionality and output comprehensively. It could mention it as a read-only operation, but the field list suffices.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With zero parameters, the description adds value by detailing the output fields, compensating for the absence of an output schema. It provides richer semantics than the empty input schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool enumerates all known cron jobs with a last-run summary, specifying the returned fields (id, name, schedule, last run time + status, etc.). It distinguishes itself from sibling tools like find_overdue_jobs and find_silent_failures, which target specific subsets.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
While the description implies general-purpose enumeration, it does not explicitly state when to use this tool versus siblings like get_job_status or get_job_runs. No exclusion criteria or alternative recommendations are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Most recent N log lines for a job (newest last).
| Name | Required | Description | Default |
|---|---|---|---|
| job_id | Yes | ||
| lines | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. Only states the result order and count, but omits read-only hint, error handling, or limitations like max lines.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, no wasted words. Efficient but could include more contextual info without becoming verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Adequate for a simple tool with 2 parameters and no output schema. Lacks details on behavior for edge cases and result format, but core purpose is clear.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%. Description hints at 'lines' parameter ('N log lines') but does not explain 'job_id' or provide format/constraints for either parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the action ('Most recent N log lines'), resource ('a job'), and ordering ('newest last'). Distinguishes from siblings like get_job_status or list_jobs.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool vs siblings. Does not mention exclusions or alternatives despite related tools (e.g., find_silent_failures, get_job_runs).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v0.3.0find_overdue_jobsfind_silent_failuresget_job_runsget_job_statuslist_jobstail_job_logsScored across 6 tools
Each tool addresses a distinct aspect of job monitoring: overdue detection, silent failure detection, run history, job status, job listing, and log tailing. No overlap in purpose.
All tool names follow a consistent verb_noun pattern with underscores, using clear verbs like find, get, list, and tail. No mixing of conventions.
Six tools cover the core functionality of a job monitoring server: listing, anomaly detection, status, logs. Neither too few nor too many for the scope.
The tool set provides complete coverage for monitoring cron jobs: discovery, anomaly detection (overdue and silent failures), status, history, and logs. No obvious gaps for a read-only monitoring use case.
Monitoring that agents set up for themselves: cron jobs, CI/CD pipelines and AI agent runs.
Control plane for autonomous software labor. Agents claim objectives over MCP with audit trail.
Personal assistant MCP server with search, execute, packages, jobs, secrets, and integrations.
MCP server for AI agents to plan, verify, and deploy Cloudflare-native apps.
