"Weights & Biases" matching MCP servers:

An MCP server that provides AI trading agents with persistent, outcome-weighted memory to learn from historical performance and detect behavioral biases. It enables agents to automatically adjust strategies and optimize position sizing based on context-aware recall of past trade outcomes.

A decision structure analysis engine that transforms emotional dilemmas into structured frameworks by identifying variables, constraints, and strategy paths. It helps users evaluate risk distributions and cognitive biases without offering subjective advice or definitive answers.

Enables enhanced decision-making through hierarchical LLM analysis, using three specialized critique agents (positive, neutral, negative) that analyze proposals in parallel and synthesize them into comprehensive, actionable insights. Helps overcome single-model biases by providing multi-perspective evaluation of complex ideas and proposals.

Audits decisions for cognitive biases through an adversarial framework, exposing tools like analyze_decision, list_biases, and get_bias via MCP.