Horizon Fidelity Monitor

Tracks the structural health of multi-turn conversations by measuring properties LLMs can't see from the inside: semantic drift, information gain, token waste, temporal desync, and causal reachability. Exposes three MCP tools (new_conversation, process_turn, configure_session) that let Claude or Cursor monitor conversation quality in real time and trigger interventions when fidelity drops. Built on information theory and category theory research showing 39% accuracy degradation after five turns in typical agent conversations. Works as a hosted SSE endpoint, a pip-installable library, or a local stdio server. Wraps OpenAI, Anthropic, and LangChain SDKs to inject monitoring into existing agents. Returns 29 signals per turn including circadian cognitive load factors and estimated conversation lifespan before collapse.