"author:alphaparkinc" matching MCP servers:

Enables autonomous agents to detect under-specified user goals, gauge reversibility and risk, and present minimal multiple-choice clarifications before committing to high-stakes execution.

Enables AI agents to run structured multi-persona debates over high-stakes decisions and merge their positions into an explainable consensus, complete with logged dissenting minority opinions. It runs as a zero-dependency Python MCP server or importable module, compatible with Claude Desktop, Cursor, and custom agent frameworks.

Enables zero-dependency statistical analysis of numerical and time-series streams, flagging outliers via standard Z-score, modified median absolute deviation (MAD), and Tukey IQR fences. Also supports trend forecasting with Holt linear smoothing, multivariate gradient-descent regression, Welch's t-test hypothesis testing, and Power Iteration PCA dimensionality reduction through a native MCP stdio interface.

Enables agents to run distributed consensus and coordination primitives over MCP, including Raft leader election with terms and quorum validation, LWW CRDT state reconciliation, vector-clock causal stamping, token-bucket gossip dissemination, and two-phase commit atomic coordination. Runs entirely on the Python standard library with zero external dependencies and JSON-RPC 2.0 stdio transport.