"author:alphaparkinc" matching MCP servers:

Enables aspect-level sentiment mining from customer reviews and produces satisfaction radar breakdowns with structured output for AI agents.

Enables users to transform raw multi-source enterprise findings into complete, structured strategic documents, auto-generating executive summaries, risk registers, decision matrices, and appendix citations. It runs as a zero-dependency Python MCP server that works with Claude Desktop, Cursor, and other MCP-compatible clients.

Analyzes customer interview transcripts to surface recurring pain points and synthesize actionable roadmap themes through MCP, with deterministic, dependency-free execution.

Enables agile sprint backlog velocity calculation and milestone completion prediction through the Model Context Protocol, integrating with MCP-compliant clients for deterministic, low-latency forecasting.

Enables AI agents and developers to run deterministic, multi-perspective synthetic consumer focus groups and analyze market sentiment through JSON-RPC/MCP tools.