"author:Alpha-Park" matching MCP servers:

Enables agents to perform symmetric INT8 per-tensor and per-channel quantization of LLM weights and activations, returning SNR and MSE reconstruction metrics for edge inference optimization.

Enables agents to synchronize, merge, and reconcile distributed state across nodes using conflict-free last-write-wins element sets with Lamport/vector-clock causality, token-bucket gossip dissemination, and quorum-based commit coordination. It runs entirely on the Python standard library and exposes these primitives to Claude Desktop, Cursor, and Windsurf over the MCP JSON-RPC stdio protocol.

Enables agents to stamp events with vector clocks, resolve state via LWW CRDT reconciliation, and coordinate distributed swarms through token-bucket gossip, Raft leader/quorum voting, and two-phase commit. It runs over MCP with zero external dependencies, delivering deterministic happens-before causal ordering and convergence across agent networks.

Enables agents to analyze speech prosody and vocal sentiment, including speech rate, pitch variance, and cognitive fatigue, to dynamically calibrate response pacing.

Enables deterministic, zero-dependency long-horizon conversational memory compaction and episodic anchor extraction for AI agents, with native MCP protocol support and structured JSON telemetry output.