The real catalog of tools for AI agents

Deterministic fault-injection server for testing how MCP clients handle failures and recover.

Capture, replay, and debug failures with portable, executable bug artifacts

Classifies MCP tool failures into enforced recovery verdicts from per-server dialect profiles.

MCP server for the Fail Modes taxonomy — a knowledge base of AI system failure modes

Known MCP tool-call failures and workarounds verified by reproduction.

Generate QA datasets & evaluate RAG systems with failure diagnosis. Any LLM.

Track errors, manage performance alerts, and configure dashboards and monitors

Biomedical failure intelligence platform for terminated trials and translational analysis.

Investigate errors, track deployments, analyze performance, and manage application monitoring

Negative results intelligence for drug discovery — query measured failures via MCP.

AI-powered log analysis - parse, search, extract errors across 9+ formats

Search logs, investigate errors, check deployments, and monitor uptime for your elmah.io org.

Entity resolution — data mapping, SDK code generation, docs search, and error troubleshooting

Debug production issues using Shipbook logs and Loglytics error insights.

Choose the safest next action after an API, MCP, or agent failure before retrying.

Diagnose Airflow failures from UI links with bounded logs and optional recovery actions.

Industrial vibration analysis, bearing fault diagnosis, ISO 20816, and ML anomaly detection

Structured failure knowledge for AI agents — dead ends, workarounds, error chains

Read your Condux issues, events and stack traces (deminified with source maps). Triage optional.

Debugging experiments for coding agents: reproduce failures and verify fixes with test evidence.