mcp-architect
Stop pasting your file tree into Claude. Give any AI assistant real architectural understanding of a codebase — local, private, zero‑config.
README
🏛️ mcp-architect
Stop pasting your file tree into Claude. Give any AI assistant real architectural understanding of a codebase — local, private, zero‑config.
AI coding assistants are great at files but blind to architecture. Every session you re‑explain the structure, paste the file tree, and hope it guesses your module boundaries right. mcp-architect is an MCP server that hands your assistant a structured map of any codebase — tech stack, dependency graph, hotspots, and module summaries — computed 100% locally with no API keys and no model required.
It works with Claude Desktop, Cursor, Windsurf, Cline, or any MCP client.
Why
| Without mcp-architect | With mcp-architect |
|---|---|
| "Here's my file tree, please figure out the structure…" | architecture_overview → stack, entry points, structure in one call |
| AI guesses how modules relate | dependency_graph → real import graph + circular‑dependency detection |
| "Which files matter?" | hotspots → largest, most complex, most‑changed, highest‑risk |
| Re‑explaining a package every time | explain → classes, functions, and deps of any folder |
Everything runs on your machine. Your code never leaves it.
Quickstart
1. Add it to your MCP client
Claude Desktop — edit claude_desktop_config.json:
{
"mcpServers": {
"architect": {
"command": "uvx",
"args": ["mcp-architect"]
}
}
}
No PyPI yet? Run straight from source:
{ "mcpServers": { "architect": { "command": "uvx", "args": ["--from", "git+https://github.com/kannajune/mcp-architect", "mcp-architect"] } } }
Restart your client. That's it — no keys, no model download.
2. Ask your assistant
"Use the architect tools to give me an overview of
~/code/my-app, then show me its dependency graph and the highest‑risk files."
What you get
# Architecture Overview — my-app
**151 files · 17,368 lines of code**
## Languages
- **Python** — 93 files, 13,683 LOC
- **TypeScript** — 23 files, 3,120 LOC
## Frameworks / key libraries
- FastAPI
- React
- Tailwind CSS
## Entry points
- main.py
# Dependency Graph — my-app
**118 modules · 172 internal import edges**
## Most depended-upon (architectural hubs)
- `app.signals.signal_parser` — imported by 12 modules
- `app.core.integrations_registry` — imported by 11 modules
## Circular dependencies
✅ no circular dependencies found
Tools
| Tool | What it tells the AI |
|---|---|
architecture_overview |
Languages, frameworks, ecosystems, size, top‑level structure, entry points |
dependency_graph |
Internal import graph, architectural hubs, circular dependencies |
hotspots |
Largest / most complex / most‑changed (git) / highest‑risk files |
explain |
Deep‑dive a folder or file: classes, functions, external deps |
Design principles
- Zero heavy dependencies. Pure Python standard library for all analysis (
ast,os,re). The only runtime dep is the MCP SDK itself. Installs in seconds. - Local & private. No network calls, no telemetry, no LLM. Your source never leaves your machine.
- Language‑aware. Full AST parsing for Python; import parsing for JavaScript/TypeScript; file/LOC stats for 25+ languages.
- Decoupled core. The analysis layer (
mcp_architect.analysis) is importable and testable on its own — use it as a plain Python library too.
from mcp_architect.analysis import get_overview, get_dependency_graph
print(get_overview("~/code/my-app")["frameworks"])
The dependency and complexity analysis is heuristic — designed to give an AI useful, fast situational awareness, not to replace a full static analyzer.
Pin to one project (optional)
Set MCP_ARCHITECT_ROOT so tools default to a fixed repo and you can omit paths:
{ "mcpServers": { "architect": {
"command": "uvx", "args": ["mcp-architect"],
"env": { "MCP_ARCHITECT_ROOT": "/Users/you/code/my-app" }
} } }
Roadmap
- [ ] Mermaid dependency‑diagram output
- [ ] Layered‑architecture / boundary‑violation detection
- [ ] Go, Rust & Java import graphs
- [ ] Optional local‑LLM (Ollama) narrative summaries
- [ ]
comparetool for before/after architecture diffs
Contributions welcome — see CONTRIBUTING.
Contributing
PRs and issues welcome! Run the tests with:
pip install -e ".[dev]"
pytest
License
MIT © Kannan Dharmalingam
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