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Track structured reasoning with confidence, branches, and revisions for complex problem solving.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "crash-mcp" yet — see the docs or source repo.
Use structured reasoning to analyze this Python service startup failure: the logs show database connection timeouts, environment variables were recently updated, and the connection pool configuration was changed. List hypotheses step by step, assign confidence levels, branch when needed, and revise conclusions when contradictions appear.
A step-by-step diagnostic analysis with key hypotheses, confidence levels, branches, and a revised most likely root cause.
Perform a structured performance analysis of this data-processing code. Identify bottlenecks step by step, compare optimization options by priority and risk, and provide a confidence level for each judgment. If an initial assumption proves wrong, explicitly revise it.
A clear optimization reasoning trail explaining bottlenecks, alternative approaches, and the final recommendation.
I need a rollout plan for a new feature. Break it down with structured reasoning across requirement clarification, technical approach, potential risks, test validation, and rollback planning. For each step, provide the conclusion, evidence, confidence level, and alternative branches when necessary.
An actionable technical plan analysis with phased conclusions, supporting rationale, risk branches, and validation recommendations.
Automatically analyze program crashes with GDB to speed up root-cause diagnosis.
Build, debug, and manage software tasks with natural language across LLMs.
Help AI agents navigate, search, and understand codebases and change history.
Compare answers from multiple LLMs side by side and spot disagreements fast.
Run dev checks and get compact error summaries for faster debugging.
Run deep research and grouped tasks in parallel for faster execution.