Side-by-side comparison of two Model Context Protocol servers — install paths, tools, quality signals, and directory engagement so you can pick the right one for Claude, Cursor, and other MCP clients.
LLM character consistency engine — generates structured JSON constraints from 4 multiple-choice questions about an AI's psychology. Drop the JSON into any LLM's system prompt to prevent persona drift; reduces inference cost from retries. 160,000 personality patterns; works with any LLM.
A high-level framework for building MCP servers in Python