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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.
Give AI agents persistent memory, searchable knowledge, and automatic consolidation.
Local-first MCP tool for shared agent memory sync and focus conflict warnings.
Connect AI apps to a shared knowledge graph for consistent retrieval and reasoning.
Connect to the mcp API via MCP to extend AI tool capabilities.
Govern and audit AI agents with safety checks, reasoning, and session controls.