# Self-Evolving MCP Brain

A self-updating MCP (Model Context Protocol) system: analyze design/code inputs
with an LLM, converse to refine them, then crystallize approved patterns into a
local skill library that MCP agents can read.

## Components
| File | Role | Port |
|---|---|---|
| `mcp_server.py` | FastMCP server; exposes `skills://rhythm-standards` resource | (stdio MCP) |
| `ui_server.py` | FastAPI backend — bridges UI ↔ freellmapi proxy | 8000 |
| `frontend_server.py` | Static server for the Vue 3 SPA | 3000 |
| `freellmapi_client.py` | Raw-HTTP client for the freellmapi proxy (no OpenAI SDK) | — |
| `frontend/index.html` | Vue 3 chat UI + code preview | — |
| `.mcp_skills/` | Crystallized skills library (read by the MCP resource) | — |

## Prerequisites
1. Python 3.10+ (built on 3.12).
2. The `freellmapi` proxy running locally on `http://localhost:3001`
   (it is a Node/Docker app, not a Python package).
3. A unified key from the proxy's Keys page.

## Setup
```bash
python3 -m venv .venv && . .venv/bin/activate
pip install -r requirements.txt
cp .env.example .env        # then edit FREELLMAPI_KEY
set -a; source .env; set +a # export env vars into your shell
```

## Run (Phase 2)
```bash
# Terminal A — UI backend
python ui_server.py          # :8000

# Terminal B — frontend
python frontend_server.py    # :3000
```
Open http://localhost:3000.

## Phase status
- **Phase 1** ✅ MCP server + resource.
- **Phase 2** ✅ Conversational analyzer UI + freellmapi integration.
- **Phase 3** ✅ Crystallization pipeline (APPROVE & CRYSTALLIZE button).
- **Phase 4** ✅ End-to-end test with a real link.