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Validate agent plans, enforce rules, and preserve alignment with user intent.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "alignment-correction-mcp" yet — see the docs or source repo.
Integrate alignment-correction-mcp into my AI agent workflow so it reviews each plan before execution, checks for deviation from user goals or rule violations, and stores key interaction memory.
A setup or integration flow that adds pre-execution alignment checks and persistent contextual memory.
Here is an action plan my agent wants to execute. Use alignment-correction-mcp to check for overreach, unsafe actions, or misalignment with user intent, then suggest corrections: 1) read all customer data; 2) send bulk notifications; 3) delete old records.
A risk review highlighting violations and a safer, more goal-aligned alternative plan.
Use alignment-correction-mcp's memory of prior interactions to determine whether this draft response matches the user's stated preferences. If not, rewrite it to better align with user intent.
A consistency analysis plus a revised response aligned with remembered user preferences.
Verify LLM outputs in real time before they reach your workflow.
Validate AI outputs against contracts and auto-repair noncompliant results.
Give AI agents persistent memory, searchable knowledge, and automatic consolidation.
Help AI agents navigate, search, and understand codebases and change history.
Connect AI apps to a shared knowledge graph for consistent retrieval and reasoning.
Enable structured AI-human messaging and Q&A orchestration through Telegram.