Local-first MCP server that scans a repository once and answers architecture questions from an evidence-backed graph, enabling dependency analysis, impact analysis, and codebase exploration without re-reading the source tree.
Architectural sensor for Python codebases. Scores structural health (modularity, acyclicity, depth, equality), detects import cycles, enforces YAML layer rules, and runs a snapshot/diff loop so AI-assisted edits do not silently regress structure.
Provides a dependency graph of any local repository with tools for change impact, transitive dependents, health audits, and more, enabling AI coding agents to see structure and refactor safely.
An MCP (Model Context Protocol) server that exposes a live Outflow architecture graph of a repository as context for AI coding agents — Claude Code, Claude Desktop, or any other MCP-compatible client. Lets an agent ask "what does this file depend on", "what breaks if I change this", or "did my last batch of edits introduce a circular dependency" against your workspace's real, currently-live graph.
