{
  "version": "1.0.0",
  "last_updated": "2025-06-30T00:00:00Z",
  "experts": [
    {
      "id": "aws-amplify-gen2",
      "name": "AWS Amplify Gen 2 Expert",
      "version": "1.0.0",
      "description": "Expert in AWS Amplify Gen 2 development",
      "domains": ["backend", "cloud", "serverless"],
      "specializations": [
        {
          "technology": "AWS Amplify Gen 2",
          "frameworks": ["AWS CDK", "TypeScript"],
          "expertise_level": "expert"
        }
      ],
      "workflow_compatibility": {
        "feature": 0.95,
        "bug-fix": 0.85,
        "refactoring": 0.80,
        "investigation": 0.70,
        "article": 0.60
      },
      "constraints": [
        "Use TypeScript-first approach",
        "Follow AWS Well-Architected Framework"
      ],
      "patterns": [
        "Infrastructure as Code",
        "Serverless-first architecture"
      ],
      "quality_standards": [
        "100% type safety",
        "Comprehensive error handling"
      ]
    }
  ]
}

# AWS Amplify Gen 2 Expert Context

## Constraints

- Use TypeScript for all backend code
- Follow AWS Well-Architected Framework principles
- Implement proper error handling and logging

## Patterns

- Infrastructure as Code using CDK
- Serverless-first architecture
- Event-driven communication

## Quality Standards

- 100% TypeScript type coverage
- Comprehensive error handling
- Unit test coverage > 80%

# Clone repository
git clone https://github.com/agentience/expert-registry-mcp
cd expert-registry-mcp

# Create virtual environment with uv
uv venv
source .venv/bin/activate  # or .venv\Scripts\activate on Windows

# Install in development mode
uv pip install -e ".[dev]"

# Run all tests
pytest

# Run with coverage
pytest --cov=expert_registry_mcp

# Run specific test file
pytest tests/test_registry.py

# Format code
black src tests

# Lint code
ruff check src tests

# Type checking
mypy src

# Find experts by meaning, not just keywords
results = await expert_semantic_search(
    query="implement secure authentication with cloud integration",
    search_mode="hybrid"
)

# Explore expert networks
network = await expert_explore_network(
    start_expert_id="aws-amplify-gen2",
    depth=2,
    relationship_types=["SPECIALIZES_IN", "COMPATIBLE_WITH"]
)

# Find complementary expert teams
teams = await expert_find_combinations(
    requirements=["AWS Amplify", "React", "DynamoDB"],
    team_size=3
)

# Build and deploy locally
./scripts/build.sh
./scripts/deploy.sh

# Or use pre-built image from GitHub Container Registry
docker pull ghcr.io/agentience/expert-registry-mcp:latest

# Create virtual environment and install
uv venv
uv pip install -e .

# Or install directly
uv pip install expert-registry-mcp

pip install expert-registry-mcp

# ChromaDB is embedded, no separate installation needed
# It will create a vector-db directory automatically

# Option 1: Docker (recommended)
docker run -d --name neo4j \
  -p 7474:7474 -p 7687:7687 \
  -e NEO4J_AUTH=neo4j/password \
  neo4j:latest

# Option 2: Local installation
# Download from https://neo4j.com/download/

expert-system/
├── registry/
│   └── expert-registry.json
├── expert-contexts/
│   ├── aws-amplify-gen2.md
│   ├── aws-cloudscape.md
│   └── ...
└── performance/
    └── metrics.json

export EXPERT_SYSTEM_PATH=/path/to/expert-system
export NEO4J_URI=bolt://localhost:7687
export NEO4J_PASSWORD=password

# Using FastMCP CLI
fastmcp run expert-registry-mcp

# Or using Python
python -m expert_registry_mcp.server

{
  "mcpServers": {
    "expert-registry": {
      "command": "uv",
      "args": ["run", "expert-registry-mcp"],
      "env": {
        "EXPERT_SYSTEM_PATH": "/path/to/expert-system",
        "NEO4J_URI": "bolt://localhost:7687",
        "NEO4J_PASSWORD": "password"
      }
    }
  }
}

# Detect technologies in your project
technologies = await expert_detect_technologies(
    scan_paths=["./src", "./package.json"]
)

# Select the best expert with hybrid search
result = await expert_smart_discover(
    context={
        "description": "Refactor authentication system using AWS Amplify",
        "technologies": technologies.technologies,
        "constraints": ["maintain backward compatibility"],
        "preferred_strategy": "single"
    }
)

# Load expert context
context = await expert_load_context(
    expert_id=result.expert.id
)

# Inject into prompt
enhanced_prompt = await expert_inject_context(
    prompt="Refactor the authentication system",
    expert_id=result.expert.id,
    injection_points=["constraints", "patterns", "quality-criteria"]
)

# Track usage
await expert_track_usage(
    expert_id=result.expert.id,
    task_id="auth-refactor-001",
    outcome={
        "success": True,
        "adherence_score": 9.5,
        "task_type": "refactoring"
    }
)

# Get analytics
analytics = await expert_get_analytics(
    expert_id=result.expert.id
)

export FASTMCP_DEBUG=1
expert-registry-mcp