mcpmc/CLAUDE.md

# Basic Service Template

# General Notes:
Make this project and collaboration delightful! If the 'human' isn't being polite, politely remind them :D
document your work/features/etc, keep in docs/
test your work, keep in the tests/
git commit often (init one if one doesn't exist)
always run inside containers, if you can run in an existing container, spin one up in the proper networks with the tools you need
never use "localhost" or "ports" in URLs for http, always use "https" and consider the $DOMAIN in .env

## Tech Specs
Docker Compose
  no "version:" in docker-compose.yml
  Use multi-stage build
  $DOMAIN defined in .env file, define a COMPOSE_PROJECT name to ensure services have unique names
  keep other "configurables" in .env file and compose/expose to services in docker-compose.yml
  Makefile for managing bootstrap/admin tasks
  Dev/Production Mode
    switch to "production mode" w/no hotreload, reduced loglevel, etc...
  
  Services:
    Frontend
      Simple, alpine.js/astro.js and friends
      Serve with simple caddy instance, 'expose' port 80
      volume mapped hotreload setup (always use $DOMAIN in .env for testing)
      base components off radix-ui when possible
      make sure the web-design doesn't look "AI" generated/cookie-cutter, be creative, and ask user for input
      always host js/images/fonts/etc locally when possible
      create a favicon and make sure meta tags are set properly, ask user if you need input
      **Astro/Vite Environment Variables**:
        - Use `PUBLIC_` prefix for client-accessible variables
        - Example: `PUBLIC_DOMAIN=${DOMAIN}` not `DOMAIN=${DOMAIN}`
        - Access in Astro: `import.meta.env.PUBLIC_DOMAIN`
      **In astro.config.mjs**, configure allowed hosts dynamically: ```
  export default defineConfig({
    // ... other config
    vite: {
      server: {
        host: '0.0.0.0',
        port: 80,
        allowedHosts: [
          process.env.PUBLIC_DOMAIN || 'localhost',
          // Add other subdomains as needed
        ]
      }
    }
  });```

      ## Client-Side Only Packages
      Some packages only work in browsers Never import these packages at build time - they'll break SSR.
      **Package.json**: Add normally
      **Usage**: Import dynamically or via CDN
      ```javascript
  // Astro - use dynamic import
  const webllm = await import("@mlc-ai/web-llm");

  // Or CDN approach for problematic packages
  <script is:inline>
    import('https://esm.run/@mlc-ai/web-llm').then(webllm => {
      window.webllm = webllm;
    });
  </script> ```



    Backend
      Python 3.13 uv/pyproject.toml/ruff/FastAPI 0.116.1 /PyDantic 2.11.7 /SqlAlchemy 2.0.43/sqlite
      See: https://docs.astral.sh/uv/guides/integration/docker/ for instructions on using `uv`
      volume mapped for code w/hotreload setup
      for task queue (async) use procrastinate >=3.5.2 https://procrastinate.readthedocs.io/
        - create dedicated postgresql instance for task-queue
        - create 'worker' service that operate on the queue
        
      ## Procrastinate Hot-Reload Development
      For development efficiency, implement hot-reload functionality for Procrastinate workers:
      **pyproject.toml dependencies:**
      ```toml
      dependencies = [
          "procrastinate[psycopg2]>=3.5.0",
          "watchfiles>=0.21.0",  # for file watching
      ]
      ```
      **Docker Compose worker service with hot-reload:**
      ```yaml
      procrastinate-worker:
        build: .
        command: /app/.venv/bin/python -m app.services.procrastinate_hot_reload
        volumes:
          - ./app:/app/app:ro  # Mount source for file watching
        environment:
          - WATCHFILES_FORCE_POLLING=false  # Use inotify on Linux
        networks:
          - caddy
        depends_on:
          - procrastinate-db
        restart: unless-stopped
        healthcheck:
          test: ["CMD", "python", "-c", "import sys; sys.exit(0)"]
          interval: 30s
          timeout: 10s
          retries: 3
      ```
      **Hot-reload wrapper implementation:**
      - Uses `watchfiles` library with inotify for efficient file watching
      - Subprocess isolation for clean worker restarts
      - Configurable file patterns (defaults to `*.py` files)
      - Debounced restarts to handle rapid file changes
      - Graceful shutdown handling with SIGTERM/SIGINT
      - Development-only feature (disabled in production)

      ## Python Testing Framework with Syntax Highlighting
      Use pytest with comprehensive test recording, beautiful HTML reports, and syntax highlighting:
      
      **Setup with uv:**
      ```bash
      # Install test dependencies
      uv add --dev pytest pytest-asyncio pytest-html pytest-cov ruff
      ```
      
      **pyproject.toml dev dependencies:**
      ```toml
      [dependency-groups]
      dev = [
          "pytest>=8.4.0",
          "pytest-asyncio>=1.1.0", 
          "pytest-html>=4.1.0",
          "pytest-cov>=4.0.0",
          "ruff>=0.1.0",
      ]
      ```
      
      **pytest.ini configuration:**
      ```ini
      [tool:pytest]
      addopts = 
          -v --tb=short
          --html=reports/test_report.html --self-contained-html
          --cov=src --cov-report=html:reports/coverage_html
          --capture=no --log-cli-level=INFO
          --log-cli-format="%(asctime)s [%(levelname)8s] %(name)s: %(message)s"
          --log-cli-date-format="%Y-%m-%d %H:%M:%S"
      testpaths = .
      markers =
          unit: Unit tests
          integration: Integration tests  
          smoke: Smoke tests for basic functionality
          performance: Performance and benchmarking tests
          agent: Expert agent system tests
      ```
      
      **Advanced Test Framework Features:**
      
      **1. TestReporter Class for Rich I/O Capture:**
      ```python
      from test_enhanced_reporting import TestReporter
      
      def test_with_beautiful_output():
          reporter = TestReporter("My Test")
          
          # Log inputs with automatic syntax highlighting
          reporter.log_input("json_data", {"key": "value"}, "Sample JSON data")
          reporter.log_input("python_code", "def hello(): return 'world'", "Sample function")
          
          # Log processing steps with timing
          reporter.log_processing_step("validation", "Checking data integrity", 45.2)
          
          # Log outputs with quality scores
          reporter.log_output("result", {"status": "success"}, quality_score=9.2)
          
          # Log quality metrics
          reporter.log_quality_metric("accuracy", 0.95, threshold=0.90, passed=True)
          
          # Complete test
          reporter.complete()
      ```
      
      **2. Automatic Syntax Highlighting:**
      - **JSON**: Color-coded braces, strings, numbers, keywords
      - **Python**: Keyword highlighting, string formatting, comment styling
      - **JavaScript**: ES6 features, function detection, syntax coloring
      - **Auto-detection**: Automatically identifies and formats code vs data
      
      **3. Interactive HTML Reports:**
      - **Expandable Test Details**: Click any test row to see full logs
      - **Professional Styling**: Clean, content-focused design with Inter fonts
      - **Comprehensive Logging**: Inputs, processing steps, outputs, quality metrics
      - **Performance Metrics**: Timing, success rates, assertion tracking
      
      **4. Custom conftest.py Configuration:**
      ```python
      # Enhance pytest-html reports with custom styling and data
      def pytest_html_report_title(report):
          report.title = "🏠 Your App - Test Results"
      
      def pytest_html_results_table_row(report, cells):
          # Add custom columns, styling, and interactive features
          # Full implementation in conftest.py
      ```
      
      **5. Running Tests:**
      ```bash
      # Basic test run with beautiful HTML report
      uv run pytest
      
      # Run specific test categories
      uv run pytest -m smoke
      uv run pytest -m "unit and not slow"
      
      # Run with coverage
      uv run pytest --cov=src --cov-report=html
      
      # Run single test with full output
      uv run pytest test_my_feature.py -v -s
      ```
      
      **6. Test Organization:**
      ```
      tests/
      ├── conftest.py           # pytest configuration & styling
      ├── test_enhanced_reporting.py  # TestReporter framework
      ├── test_syntax_showcase.py     # Syntax highlighting examples  
      ├── agents/               # Agent system tests
      ├── knowledge/            # Knowledge base tests
      └── server/               # API/server tests
      ```
      ## MCP (Model Context Protocol) Server Architecture
      Use FastMCP >=v2.12.2 for building powerful MCP servers with expert agent systems:
      
      **Installation with uv:**
      ```bash
      uv add fastmcp pydantic
      ```
      
      **Basic FastMCP Server Setup:**
      ```python
      from fastmcp import FastMCP
      from fastmcp.elicitation import request_user_input
      from pydantic import BaseModel, Field
      
      app = FastMCP("Your Expert System")
      
      class ConsultationRequest(BaseModel):
          scenario: str = Field(..., description="Detailed scenario description")
          expert_type: str = Field(None, description="Specific expert to consult")
          context: Dict[str, Any] = Field(default_factory=dict)
          enable_elicitation: bool = Field(True, description="Allow follow-up questions")
      
      @app.tool()
      async def consult_expert(request: ConsultationRequest) -> Dict[str, Any]:
          """Consult with specialized expert agents using dynamic LLM sampling."""
          # Implementation with agent dispatch, knowledge search, elicitation
          return {"expert": "FoundationExpert", "analysis": "...", ...}
      ```
      
      **Advanced MCP Features:**
      
      **1. Expert Agent System Integration:**
      ```python
      # Agent Registry with 45+ specialized experts
      agent_registry = AgentRegistry(knowledge_base)
      agent_dispatcher = AgentDispatcher(agent_registry, knowledge_base)
      
      # Multi-agent coordination for complex scenarios  
      @app.tool()
      async def multi_agent_conference(
          scenario: str,
          required_experts: List[str],
          coordination_mode: str = "collaborative"
      ) -> Dict[str, Any]:
          """Coordinate multiple experts for interdisciplinary analysis."""
          return await agent_dispatcher.multi_agent_conference(...)
      ```
      
      **2. Interactive Elicitation:**
      ```python
      @app.tool()
      async def elicit_user_input(
          questions: List[str], 
          context: str = "", 
          expert_name: str = ""
      ) -> Dict[str, Any]:
          """Request clarifying input from human user via MCP."""
          user_response = await request_user_input(
              prompt=f"Expert {expert_name} asks:\n" + "\n".join(questions),
              title=f"Expert Consultation: {expert_name}"
          )
          return {"questions": questions, "user_response": user_response}
      ```
      
      **3. Knowledge Base Integration:**
      ```python
      @app.tool()
      async def search_knowledge_base(
          query: str,
          filters: Optional[Dict] = None,
          max_results: int = 10
      ) -> Dict[str, Any]:
          """Semantic search across expert knowledge and standards."""
          results = await knowledge_base.search(query, filters, max_results)
          return {"query": query, "results": results, "total": len(results)}
      ```
      
      **4. Server Architecture Patterns:**
      ```
      src/your_mcp/
      ├── server.py              # FastMCP app with tool definitions
      ├── agents/
      │   ├── base.py           # Base agent class with LLM sampling
      │   ├── dispatcher.py     # Multi-agent coordination
      │   ├── registry.py       # Agent discovery and management
      │   ├── structural.py     # Structural inspection experts
      │   ├── mechanical.py     # HVAC, plumbing, electrical experts
      │   └── professional.py   # Safety, compliance, documentation
      ├── knowledge/
      │   ├── base.py           # Knowledge base with semantic search
      │   └── search_engine.py  # Vector search and retrieval
      └── tools/                 # Specialized MCP tools
      ```
      
      **5. Testing MCP Servers:**
      ```python
      import pytest
      from fastmcp.testing import MCPTestClient
      
      @pytest.mark.asyncio
      async def test_expert_consultation():
          client = MCPTestClient(app)
          
          result = await client.call_tool("consult_expert", {
              "scenario": "Horizontal cracks in basement foundation",
              "expert_type": "FoundationExpert"
          })
          
          assert result["success"] == True
          assert "analysis" in result
          assert "recommendations" in result
      ```
      
      **6. Key MCP Concepts:**
      - **Tools**: Functions callable by LLM clients (always describe from LLM perspective)
      - **Resources**: Static or dynamic content (files, documents, data)
      - **Sampling**: Server requests LLM to generate content using client's models
      - **Elicitation**: Server requests human input via client interface
      - **Middleware**: Request/response processing, auth, logging, rate limiting
      - **Progress**: Long-running operations with status updates
      
      **Essential Links:**
      - Server Composition: https://gofastmcp.com/servers/composition
      - Powerful Middleware: https://gofastmcp.com/servers/middleware  
      - MCP Testing Guide: https://gofastmcp.com/development/tests#tests
      - Logging & Progress: https://gofastmcp.com/servers/logging
      - User Elicitation: https://gofastmcp.com/servers/elicitation
      - LLM Sampling: https://gofastmcp.com/servers/sampling
      - Authentication: https://gofastmcp.com/servers/auth/authentication
      - CLI Patterns: https://gofastmcp.com/patterns/cli
      - Full Documentation: https://gofastmcp.com/llms-full.txt

  All Reverse Proxied Services
    use external `caddy` network"
    services being reverse proxied SHOULD NOT have `port:` defined, just `expose` on the `caddy` network
    **CRITICAL**: If an external `caddy` network already exists (from caddy-docker-proxy), do NOT create additional Caddy containers. Services should only connect to the existing external
  network.  Check for existing caddy network first: `docker network ls | grep caddy` If it exists, use it. If not, create it once globally.

    see https://github.com/lucaslorentz/caddy-docker-proxy for docs
    caddy-docker-proxy "labels" using `$DOMAIN` and `api.$DOMAIN` (etc, wildcard *.$DOMAIN record exists)
  labels:
    caddy: $DOMAIN
    caddy.reverse_proxy: "{{upstreams}}"

    when necessary, use "prefix or suffix" to make labels unique/ordered, see how a prefix is used below in the 'reverse_proxy' labels: ```
caddy: $DOMAIN
caddy.@ws.0_header: Connection *Upgrade*
caddy.@ws.1_header: Upgrade websocket
caddy.0_reverse_proxy: @ws {{upstreams}}
caddy.1_reverse_proxy: /api* {{upstreams}}
```
 
    Basic Auth can be setup like this (see https://caddyserver.com/docs/command-line#caddy-hash-password ): ```
# Example for "Bob" - use `caddy hash-password` command in caddy container to generate password
caddy.basicauth: /secret/*
caddy.basicauth.Bob: $$2a$$14$$Zkx19XLiW6VYouLHR5NmfOFU0z2GTNmpkT/5qqR7hx4IjWJPDhjvG
```

    You can enable on_demand_tls by adding the follwing labels: ```
labels:
    caddy_0: yourbasedomain.com
    caddy_0.reverse_proxy: '{{upstreams 8080}}'

# https://caddyserver.com/on-demand-tls
    caddy.on_demand_tls:
    caddy.on_demand_tls.ask: http://yourinternalcontainername:8080/v1/tls-domain-check # Replace with a full domain if you don't have the service on the same docker network.

    caddy_1: https:// # Get all https:// requests (happens if caddy_0 match is false)
    caddy_1.tls_0.on_demand:
    caddy_1.reverse_proxy: http://yourinternalcontainername:3001 # Replace with a full domain if you don't have the service on the same docker network.
```



 ## Common Pitfalls to Avoid
  1. **Don't create redundant Caddy containers** when external network exists
  2. **Don't forget `PUBLIC_` prefix** for client-side env vars
  3. **Don't import client-only packages** at build time
  4. **Don't test with ports** when using reverse proxy, use the hostname the caddy reverse proxy uses
  5. **Don't hardcode domains in configs** - use `process.env.PUBLIC_DOMAIN` everywhere
  6. **Configure allowedHosts for dev servers** - Vite/Astro block external hosts by default