"Elastic" matching MCP servers:

Enables users and autonomous agents to calculate dynamic price elasticity of demand and identify marginal revenue maxima through a zero-dependency, MCP-compatible Python tool.

Enables personal AI agents and users to reinforce habits, triage decisions, and prevent streak-break abandonment by generating adaptive micro-fallbacks and elastic recommendations.

Unifies 7,283+ detection rules from Sigma, Splunk ESCU, Elastic, and KQL into a single queryable interface via MCP, with a web dashboard and autonomous agent pipeline for detection engineering.

Enables personal AI agents to triage inbox items by classifying their actionability, effort duration, and urgency, producing a prioritized queue with elastic recommendations. It plugs into MCP-compliant clients such as Claude Desktop and Cursor to protect focus and reduce attention fragmentation.

Enables personal agents to track habits and receive adaptive micro-fallbacks that degrade gracefully when a streak is missed, rather than letting the user abandon it. Surfaces prioritized queues and elastic recommendations through MCP-compatible clients like Claude Desktop, Cursor, and Windsurf.

Enables autonomous agents to calculate dynamic price elasticity of demand and derive marginal revenue maxima from query inputs. It returns structured JSON outputs with execution telemetry for economic modeling and pricing optimization.