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Search AI failure modes taxonomy to assess factuality, alignment, planning, and risks.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "failmodes-mcp" yet — see the docs or source repo.
Search the Fail Modes taxonomy for failure modes most related to LLM hallucinations and factuality errors, and organize them by definition, typical behaviors, and common trigger scenarios.
A list of hallucination- and factuality-related failure modes with definitions, symptoms, and scenario notes.
Search the Fail Modes taxonomy for failure modes related to AI alignment, safety boundaries, and goal drift, then summarize them into a risk research brief and highlight their differences.
A brief on alignment risks covering key failure modes, distinctions, and research priorities.
Using failure modes in the Fail Modes taxonomy related to planning errors, tool misuse, and instruction drift, list evaluation test cases and checkpoints suitable for product assessment.
A checklist of evaluation test cases that can be used to design quality tests and acceptance criteria.
Build, debug, and manage software tasks with natural language across LLMs.
Let AI perform calculations, search knowledge, and handle basic email tasks.
Analyze disk images with AI through MCP for fast forensic investigation.
Connect AI apps to a shared knowledge graph for consistent retrieval and reasoning.
Access Falai tools and services through a standardized MCP interface.
Provide AI agents with coding standards, testing, planning, and requirements guidance.