# 1. Clone
git clone https://github.com/architecture-pattern/architecture-pattern-mcp.git
cd architecture-pattern-mcp

# 2. Add your API key
export GENERATOR_API_KEY=your_key_here

# 3. Start (Docker builds + starts everything)
docker compose -f docker/docker-compose.yml up --build

# 4. Demo
make client

# Install (one-time)
uv pip install -e .

# Run as stdio subprocess — pass API key via env
claude mcp add architecture-pattern \
  -e GENERATOR_API_KEY=your_key \
  -e GENERATOR_PROVIDER=openai \
  -- architecture-pattern-mcp --transport stdio

claude mcp add --scope project architecture-pattern \
  -e GENERATOR_API_KEY=your_key \
  -- architecture-pattern-mcp --transport stdio

# Terminal 1: start the server
docker compose -f docker/docker-compose.yml up --build
# or locally:
uv run python -m src.main --port 8050

# Terminal 2: add to ~/.config/opencode/opencode.json

{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "architecture-pattern": {
      "type": "remote",
      "url": "http://localhost:8060/mcp"
    }
  }
}

# Install (one-time)
uv pip install -e .

[mcp_servers.architecture-pattern]
command = "architecture-pattern-mcp"
args = ["--transport", "stdio"]

[mcp_servers.architecture-pattern.env]
GENERATOR_API_KEY = "your_key"
GENERATOR_PROVIDER = "openai"

codex mcp add architecture-pattern \
  -e GENERATOR_API_KEY=your_key \
  -- architecture-pattern-mcp --transport stdio

skills/architecture-pattern-mcp/
├── SKILL.md                 # Discovery, critical rules, decision guide
└── references/
    ├── tools.md             # All 9 tool signatures and output schemas
    └── workflows.md         # 4 worked examples, 4 prompts, best practices

Build a scalable ETL pipeline for IoT sensor data: ingest 10k events/sec
from Kafka, parse JSON, enrich with geolocation from Redis, write to InfluxDB
and S3.

Call analyze_architecture with:
  requirements: "Real-time data processing pipeline for 10k events/sec IoT sensor data"
  domain: "data-processing"

Call generate_architecture with:
  requirements: "ETL pipeline: Kafka → JSON parse → Redis geo-enrich → InfluxDB + S3"
  domain: "data-processing"
  selected_patterns: ["pipe-and-filter"]

Call evaluate_architecture with:
  architecture: { ... paste a design dict here ... }
  criteria: "scalability, reliability"

Call list_architecture_patterns()  # all 40 patterns
Call list_architecture_patterns(category="messaging")  # filter by category
Call get_architecture_pattern(name="event-driven")   # full pattern JSON

# Step 1: start the job
Call submit_architecture_design_job with:
  requirements: "ETL pipeline for IoT: Kafka → JSON → Redis geo-enrich → InfluxDB + S3"
  domain: "data-processing"

# Step 2: poll every 10-30 seconds
Call get_architecture_design_status with:
  job_id: "<job_id from step 1>"

# → status is "pending" | "running" | "completed" | "failed" | "cancelled"
# When status is "completed", the full design is in result.design
# When status is "failed", the error is in result.error

import asyncio, aiohttp

SERVER = "http://localhost:8060/mcp"
POLL_EVERY = 15  # seconds

async def main():
    async with aiohttp.ClientSession() as sess:
        # Start
        async with sess.post(SERVER, json={
            "jsonrpc": "2.0",
            "method": "tools/call",
            "params": {
                "name": "submit_architecture_design_job",
                "arguments": {
                    "requirements": "ETL pipeline for IoT: Kafka → JSON → Redis → InfluxDB + S3",
                    "domain": "data-processing",
                }
            },
            "id": 1
        }) as resp:
            job_id = (await resp.json())["result"]["content"][0]["data"]["job_id"]

        print(f"Job started: {job_id}")

        # Poll
        while True:
            await asyncio.sleep(POLL_EVERY)
            async with sess.post(SERVER, json={
                "jsonrpc": "2.0",
                "method": "tools/call",
                "params": {"name": "get_architecture_design_status", "arguments": {"job_id": job_id}},
                "id": 2
            }) as resp:
                result = (await resp.json())["result"]["content"][0]["data"]
                print(f"  status={result['status']}")
                if result["status"] in ("completed", "failed", "cancelled"):
                    break

        print(result.get("result", result))  # full design when completed

docker compose -f docker/docker-compose.yml up --build   # Terminal 1
make client-async                                      # Terminal 2

Call list_architecture_patterns() with no filters to see all patterns.

Show me details about the event-driven architecture pattern.