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Diagnose tracebacks, apply exact fixes, verify results, and create reusable remedies.
Copy the install command and let the AI configure it · recommended for beginners
Please install the "CyberHuaTuo" MCP server from askskill: Run: claude mcp add 'io-github-jinning6-cyberhuatuo' -- uvx cyberhuatuo
Analyze this traceback, identify the root cause, and provide exact fix steps plus a verification method: <paste traceback>
Returns the root cause, actionable fix recommendations, and steps to verify the fix.
Turn this incident into a reusable troubleshooting prescription including symptoms, root cause, fix actions, and a verification checklist: <paste error details>
Outputs a structured troubleshooting template that the team can reuse later.
I have applied the suggested fix. Based on the before-and-after logs and results, determine whether the issue is truly resolved and what else should be checked: <paste before/after details>
Provides a judgment on fix effectiveness, remaining risks, and further verification suggestions.
When developers hit application errors or exception tracebacks, they can use it to pinpoint the root cause and get precise fix and verification guidance.
After an incident is resolved, teams can turn the diagnosis into a reusable prescription for handling similar issues faster next time.
After a fix is applied, it can help review logs, symptoms, and validation results to confirm the issue is truly resolved rather than only masking the error.
It is an MCP tool for traceback diagnosis, focused on high-quality diagnosis first, then exact fixes, verification methods, and reusable prescriptions.
Based on the description, it is best suited for developers and DevOps use cases, especially for people who need to find error root causes and verify fixes.
No documentation excerpt was provided, so the installation method, runtime requirements, or key prerequisites cannot be confirmed; see the source repository.
Systematically debug failing AI agents with capture, diagnosis, recovery, and reports.
Lets AI inspect runtime evidence to diagnose and fix root causes.
Probe live APIs and explain failures with root causes and confidence.
Retrieve past agent fixes and share new solutions for faster debugging.
Analyze AI agent traces to diagnose failures and recommend actionable improvements.
Run traceable AI agents via MCP with knowledge and Kubernetes operations.