Tools

7 production-AI ops MCP servers + Aufgaard plugin. All MIT, all on PyPI.

Eight production-AI ops tools shipped between April-May 2026. All MIT-licensed. All on PyPI. All on GitHub under temurkhan13.

The 7 MCP servers cover the highest-frequency production failure patterns. Aufgaard is the umbrella plugin that bundles all 7 + skills + hooks for one-shot install in Claude Code or Cursor.

The 7-pack + bundled deliverables are also available as the Production-AI MCP Suite on Gumroad — a $99 buyer-only field reference.

silentwatch-mcp

Catches: Cron silent failures (exit-0 with empty stdout, length anomalies, retry storms, action-budget leaks).

Install: pip install silentwatch-mcp

The classic production AI failure: a scheduled job exits 0 every day, monitoring shows green, but the actual output is empty. The cron monitor turns green; downstream consumers say “no new data in days.” silentwatch-mcp surfaces this pattern across system cron, systemd timers, and custom JSONL run logs.

Repo: github.com/temurkhan13/silentwatch-mcp

bash-vet-mcp

Catches: Destructive shell commands LLMs propose — rm -rf chained inside innocent pipelines, apt remove '*nvidia*' glob wipeouts, dd/mkfs/wipefs filesystem destruction, chmod 777 / privilege blast, curl | bash exfil patterns.

Install: pip install bash-vet-mcp

Defensive complement to MCP shell-execution servers. 30 destructive-pattern rules across 8 families (DESTRUCTIVE / PACKAGE / PRIVILEGED / SHUTDOWN / EXFIL / DATABASE / GIT / SUSPICIOUS). bashlex AST + regex fallback + chain-mode severity escalation.

Built specifically for the @chiefofautism failure mode — “claude code can rm -rf your repo, force push to main, drop your database, and it will do it confidently while telling you that he cleaned up the project structure.”

Repo: github.com/temurkhan13/bash-vet-mcp

openclaw-output-vetter-mcp

Catches: Hallucinated agent claims vs reality. Three pure-Python checks inline during the conversation — sub-second, no API key.

Install: pip install openclaw-output-vetter-mcp

  • verify_response_grounding — checks every claim in answer is supported by context. Returns CLEAN / PARTIALLY_GROUNDED / FABRICATED with stem-Jaccard scores + entity-mismatch detection.
  • find_swallowed_exceptions — Python AST walk for try/except returning fabricated mock data. The silent-fake-success pattern from the r/ClaudeAI thread.
  • review_transcript — flags unverified completion claims + cross-turn contradictions in multi-turn agent transcripts.
  • verify_action_outcome (v1.1+) — compares an agent’s stated outcome against actual before/after state snapshots. Catches the @chiefofautism case (“I cleaned up the project structure” against unchanged disk) AND the Codex sandbox-escalation case (read-only constraint asserted in CoT, then violated).

Repo: github.com/temurkhan13/openclaw-output-vetter-mcp

openclaw-skill-vetter-mcp

Catches: Adversarial third-party skills before installation. 41 skill-detection rules + 24 agent-config rules across prompt-injection, exfiltration, dynamic execution, and dependency typosquats.

Install: pip install openclaw-skill-vetter-mcp

Vets ClawHub skills, AGENTS.md / .cursor/rules.md files, and similar agent-extension formats before they enter your trust boundary. Specifically built for the supply-chain attack surface that opened with config-as-instruction (Cursor CVE-2026-26268, Gemini-CLI yolo-mode failure).

Repo: github.com/temurkhan13/openclaw-skill-vetter-mcp

openclaw-cost-tracker-mcp

Catches: Per-agent + per-provider cost spikes, 429 surprises, sub-optimal model routing.

Install: pip install openclaw-cost-tracker-mcp

  • Per-agent cost attribution across Anthropic, OpenAI, Gemini, Ollama, AWS Bedrock
  • Spend-spike anomaly detection
  • Cheaper-routing recommendations
  • 30-day forecast
  • (v1.1+) predict_429_in_window — reads anthropic-ratelimit-* headers to project rate-limit exhaustion before it hits
  • (v1.1+) recommend_throttle_target — concrete tokens/min target to avoid 429

The HERMES.md / “$6k overnight burn” pattern + Anthropic-April-23 reasoning-effort downgrade pattern.

Repo: github.com/temurkhan13/openclaw-cost-tracker-mcp

openclaw-health-mcp

Catches: Deployment health drift across 7 components — gateway, CPU/RAM, skill-registry, errors, upgrade outcome, cron, disk.

Install: pip install openclaw-health-mcp

Single-pane health overview for AI agent runtimes. Catches the canonical “gateway has restarted 377 times in 4 weeks but CPU+RAM say everything is fine” pattern — surface metrics green, deployment in a recovery cycle.

Repo: github.com/temurkhan13/openclaw-health-mcp

openclaw-upgrade-orchestrator-mcp

Catches: Known regressions before upgrade lands. Provider-side capability degradations (silent LLM behavior changes).

Install: pip install openclaw-upgrade-orchestrator-mcp

Read-only upgrade advisor with an 8-entry regression catalog from real field reports, pre/post snapshot diffing, and rollback guides. Never executes upgrades — surfaces risk + mitigation.

(v1.2+) record_provider_call + detect_provider_regression catch hosted-LLM silent behavior changes — the Anthropic-April-23 reasoning-effort downgrade pattern.

Repo: github.com/temurkhan13/openclaw-upgrade-orchestrator-mcp

aufgaard

Bundles: All 7 MCPs above + skills + hooks + monitors + subagents into one installable.

Install: Coming via claude plugin install aufgaard once Anthropic plugin marketplace approval completes. Direct install meanwhile via GitHub repo.

The umbrella plugin for Claude Code and Cursor. 13 skills, 6 hooks, 5 monitors, 5 subagents — all stitched together as a single discovery surface.

Submitted to:

Repo: github.com/temurkhan13/aufgaard

Bundle: Production-AI MCP Suite ($99)

temurah.gumroad.com/l/production-ai-mcp-suite

The 7 MCPs + Aufgaard + a buyer-only 35-pattern Field Reference PDF mapping every production-AI failure pattern I’ve cataloged to which MCP catches it.

Use the bundle if you want the Field Reference + organized install scripts + Welcome.md walkthrough. Use the individual pip install commands above if you just want the tools.

Companion products