An MCP server that exposes causal inference methods (difference-in-differences, synthetic control, propensity matching, and assumption checks) as callable tools, enabling AI agents to run deterministic statistical analyses instead of computing them inline.
MCP server for the DAG Studio causal-inference engine, enabling AI agents to construct, analyze, and validate causal directed acyclic graphs (DAGs) through tools for analysis, data simulation, and code generation.
A statistical analysis MCP server offering 30 tools for descriptive statistics, hypothesis tests, regression, and time series, all returning Markdown reports with automatic interpretations to enable AI agents to perform comprehensive data analysis.
A Model Calling Protocol server that enables LLMs to perform rigorous Bayesian analysis and probabilistic reasoning, including inference, model comparison, and predictive modeling with uncertainty quantification.
A scientific experiment log MCP server for AI agents that stores predictions, causal claims, and verdicts, enabling queryable causal maps and calibration of intuition over diagnostics.
