Config is the same across clients — only the file and path differ.
{
"mcpServers": {
"archy": {
"args": [
"mcp"
],
"command": "archy"
}
}
}Are you the author?
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Architectural sensor for Python codebases - keeps structure honest under AI-assisted development.
Run this in your terminal to verify the server starts. Then let us know if it worked — your result helps other developers.
uvx 'archy' 2>&1 | head -1 && echo "✓ Server started successfully"
After testing, let us know if it worked:
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No known CVEs.
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Architectural sensor for Python codebases - keeps structure honest under AI-assisted development.
pip install archy
archy score . # one-shot architectural health number
archy hotspots . # refactor priority = complexity x git churn
archy mcp # expose 18 tools to Claude Code, Cursor, any MCP client

Free, MIT licensed, no commercial version planned. Built and maintained by Alex Lee.
Status: v0.29.0. Usable today via:
| Mode | Command |
|---|---|
| Inspection | archy graph, archy cycles |
| CI governance | archy check (reads archy.yaml) |
| Transitive contracts | archy contracts (reads .importlinter (canonical) or falls back to archy.yaml; requires archy[contracts]) |
| One-shot score | archy score |
| Trended score | archy score --record + archy trend |
| Refactor priority | archy hotspots (CC x git churn) |
| CI impact lookup | archy affected (git diff -> impacted modules + tests, depth-capped) |
| MCP server | archy mcp (cached: warm graph builds in seconds even on 10k+ module repos) |
| Parse cache | archy index sync / archy index clear (persistent .archy/index.db; transparent under the MCP server) |
| Agent install | archy install / archy uninstall (auto-detect Claude Code, Cursor, Codex, opencode, Continue; wire in or cleanly remove the MCP server) |
How the score is computed and how to read it: docs/SCORING.md. Benchmarks against pydantic, fastapi, flask, pytest, and archy-on-archy: docs/CASE_STUDIES.md. Design rationale and comparison with sentrux: docs/LEARNINGS.md.
archy is used in production by the projects listed in ADOPTERS.md. If you're running archy on a real codebase, please open a PR to add yourself, or file an issue and I'll add you.
I built archy because I kept watching coding agents generate code that passed review but rotted the import graph underneath. The score on a feature change would look fine; six weeks later the cycle count had doubled and nobody noticed until a refactor blew up. I wanted a single number per commit that would have caught it.
AI agents generate code at machine speed. Without a feedback loop on structural health (module coupling, import cycles, layer violations), codebases drift architecturally even when every individual change looks fine in review.
archy watches a Python codebase, builds a live module-dependency graph, and surfaces drift through a single trended score plus a handful of actionable sub-metrics. It's designed to run in CI, in pre-commit, and as an MCP server (archy mcp) so coding agents can read their own architectural impact before committing.
The agent-feedback framing is empirically supported by 2025-2026 research: the Navigation Paradox paper shows large LLM context windows do not eliminate the need for structural graph navigation, LocAgent's ablation finds graph edges materially improve code-localization accuracy, the Constraint Decay paper (arxiv:2605.06445) finds agents lose ~30 points in pass rate as architectural constrain