Archiet Xray MCP server

Archiet X-Ray

mcp-name: io.github.Anioko/archiet-xray

See what your AI sees. Your codebase is too big for any AI agent's context window. The agent reads 40 files out of 4,000, makes a change, and you have no way to know whether it respected the architecture — or quietly violated it.

X-Ray extracts the actual architecture of any repo — deterministically, no LLM, code never leaves your machine — and gives it to both you (an interactive map) and your AI agent (an MCP server + context pack).

$ python xray.py .

Archiet X-Ray v0.2.0 — your-repo
  visibility score : 78/100
  prod readiness   : 71/100 (near production-ready)
  code files       : 3,412 (1,907 read, 1,584 with elements)
  routes           : 214
  entities         : 87
  async tasks      : 31
  findings         : 6
  wrote            : .archiet/ARCHITECTURE.md
                     .archiet/AGENT_CONTEXT.md
                     .archiet/architecture.json
                     .archiet/diagrams/diagrams.html (+3 .mmd)

What you get

Production-readiness score (new in 0.2)

A deterministic 0–100 production-readiness score over 8 dimensions: route auth coverage, secrets hygiene, client token storage, data-layer discipline, test footprint, migration discipline, ops readiness (Docker/CI/.env contract), and docs/API contract. Same repo always scores the same — so the score is comparable across commits, branches, and repos, and you can put it in CI.

Each dimension reports its evidence ("142/214 statically-guarded routes carry an auth guard") and the report ends with the top fixes ranked by points lost. Dimensions that don't apply (a CLI tool has no routes) take half credit and say so — honesty over flattery.

Useful when: reviewing AI-generated code before shipping it, auditing an inherited codebase, or tracking whether your repo is drifting away from production readiness over time.

Architecture diagrams from code (new in 0.2)

generatediagrams turns any repo into Mermaid architecture diagrams**, deterministically extracted from the code itself:

Django, and Prisma models

• Module dependency graph — the real import structure, not the wiki's
• ER / domain-model diagram — entities and relations from SQLAlchemy,

• HTTP route map — the API surface grouped by prefix

You get the raw .mmd sources (paste into any README, wiki, GitHub, GitLab, Notion, Obsidian, or VS Code — they all render Mermaid natively) plus a self-contained diagrams.html viewer.

Give it to your agent (MCP)

# Claude Code (from PyPI — pip install archiet-xray)
claude mcp add archiet-xray -- archiet-xray mcp /path/to/repo

# or from a checkout
claude mcp add archiet-xray -- python /path/to/mcp_server.py /path/to/repo

Claude Desktop — download archiet-xray-.mcpb from the latest release, then double-click it (or Settings → Extensions → Install Extension). It prompts for the repository to analyze; one-click after that.

Official MCP Registry — listed as io.github.Anioko/archiet-xray, so registry-aware clients can install it by name.

Shortened. The full README is on GitHub.