Typed MCP overrides keep human commands in control of AI agents
A proposed protocol gives human operators binding instructions that survive an LLM agent's later planning cycles, without fine-tuning or model-weight access. It sends corrections as schema-checked MCP tool calls, then prepends a ranked authority context to each prompt, separating constraints, priorities, and strategies. Across nine models and eight scenarios, it reduced ambiguous directives and authority decay, with prompt overhead falling by up to 38% in the strongest models, though results varied substantially.