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.
An MCP server that gives AI agents full visibility and control over your Dagster instance, enabling autonomous monitoring, diagnosis, and remediation of data pipelines.
A server providing rigorous causal inference tools through the Model Context Protocol (MCP), offering 42 specialized causal analysis tools that cover modeling, effect estimation, attribution, root cause analysis, counterfactuals, and sensitivity analysis.
MCP server that enables AI agents to run a deterministic orchestration loop with decomposition, subagent execution, and review feedback across multiple LLM backends.
MCP server for coding-os, a cognitive operating system that gives AI agents memory, structure, and discipline. It exposes tools for project management, task tracking, knowledge graphs, and cognition traces to Claude Code and Codex.
