ontology-mcp-self-healing
Orchestrates agent interactions and manages multi-agent systems with semantic understanding.
Allows querying MySQL databases using semantic queries translated to SQL via ontology mappings.
Allows querying PostgreSQL databases using semantic queries translated to SQL via ontology mappings.
Sends alert notifications to Slack webhooks when schema changes are detected and healed.
Allows querying SQLite databases using semantic queries translated to SQL via ontology mappings.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@ontology-mcp-self-healingmonitor and auto-heal schema changes"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Self-Healing Ontology MCP Agent System
A production-ready self-healing multi-agent system that uses ontologies and MCP (Model Context Protocol) to automatically adapt when database schemas change.
Overview
This system solves a critical problem in modern AI agent deployments: when database schemas change, agent queries break. Instead of manually updating every agent and query, this system:
Monitors database schemas continuously
Detects schema changes automatically
Analyzes changes using Claude AI
Heals ontology mappings automatically
Reloads MCP tools without downtime
The result: agents continue working seamlessly even when databases evolve.
Architecture
┌─────────────┐ ┌──────────────┐ ┌─────────────┐
│ Agents │─────▶│ MCP Server │─────▶│ Database │
│ (Analytics, │ │ (Ontology) │ │ (SQLite, │
│ Support) │ │ │ │ PostgreSQL)│
└─────────────┘ └──────┬───────┘ └─────────────┘
│
▼
┌─────────────────┐
│ Schema Monitor │
│ (SHA-256 Hash) │
└────────┬────────┘
│
▼
┌─────────────────┐
│ Diff Engine │
│ (Change Detect) │
└────────┬────────┘
│
▼
┌─────────────────┐
│ Ontology Remap │
│ (Claude AI) │
└─────────────────┘Features
✅ Automatic Schema Change Detection - SHA-256 hash-based monitoring
✅ Intelligent Diff Analysis - Detects renames, additions, deletions
✅ AI-Powered Healing - Uses Claude to update ontology mappings
✅ MCP Protocol Support - Native Model Context Protocol integration
✅ Multi-Agent Support - Shared semantic understanding across agents
✅ Hot Reload - MCP server reloads without downtime
✅ Audit Logging - Complete JSON audit trail
✅ Alert Integration - Slack/Teams webhook support
✅ Production Ready - Docker, tests, error handling
Quickstart (< 5 minutes)
1. Install Dependencies
pip install -r requirements.txt2. Set Up Environment
# Create .env file
echo "ANTHROPIC_API_KEY=your_api_key_here" > .env3. Initialize Database and Ontology
# Create sample database
python scripts/init_db.py
# Generate initial ontology
python scripts/setup_ontology.py4. Run Quickstart Example
python examples/quickstart.pyYou should see:
✓ MCP Server initialized
✓ Tools generated from ontology
✓ Schema monitoring active
✓ Agent system ready
5. Test Schema Change Healing
# In another terminal, modify the database schema
sqlite3 test_database.db "ALTER TABLE customers ADD COLUMN phone TEXT;"
# Watch the system automatically detect and heal
python examples/full_system.pyInstallation
From Source
git clone https://github.com/yourusername/ontology-mcp-self-healing.git
cd ontology-mcp-self-healing
pip install -r requirements.txt
pip install -e .Using Docker
# Build and run
docker-compose up -d
# View logs
docker-compose logs -fConfiguration
Configuration is managed via config/config.yaml:
# Database Configuration
database:
type: sqlite # sqlite, postgresql, mysql
connection_string: sqlite:///./test_database.db
# Ontology Configuration
ontology:
main_file: ontologies/business_domain.owl
auto_reload: true
# Schema Monitoring
monitoring:
enabled: true
check_interval: 60 # seconds
detect_renames: true
# Auto-Healing
healing:
enabled: true
auto_approve: false # Set to true for automatic healing
claude_model: claude-3-5-sonnet-20241022
validation_enabled: true
# Alerts
alerts:
enabled: true
webhook_url: ${ALERT_WEBHOOK_URL} # OptionalUsage Examples
Basic MCP Server
from src.mcp_server.server import OntologyMCPServer
# Initialize server
server = OntologyMCPServer()
# Get available tools
tools = server.get_tools()
# Execute query
result = await server.execute_tool(
"query_order",
{"query": "all orders", "limit": 10}
)Create an Agent
from src.mcp_server.server import OntologyMCPServer
from src.agents.examples.analytics_agent import AnalyticsAgent
# Initialize MCP server
mcp_server = OntologyMCPServer()
# Create agent
agent = AnalyticsAgent(mcp_server, claude_api_key="your_key")
# Query using natural language
response = await agent.query("What are the total sales for last month?")Full Self-Healing System
from src.system.self_healing import SelfHealingAgentSystem
# Initialize system
system = SelfHealingAgentSystem()
# Start monitoring and healing
system.start()
# System runs forever, auto-healing on schema changesArchitecture Details
MCP Server
The MCP server loads OWL ontologies and generates tools dynamically:
Extracts class → table mappings
Extracts property → column mappings
Translates semantic queries to SQL
Caches queries for performance
Schema Monitor
Continuous monitoring using:
SQLAlchemy inspector for schema capture
SHA-256 hashing for change detection
Configurable check intervals
Event callbacks on changes
Diff Engine
Intelligent diff computation:
Detects table/column additions/removals
Heuristic-based rename detection
Type change detection
Detailed diff reporting
Auto Remapper
AI-powered ontology healing:
Extracts current ontology mappings
Generates LLM prompts with schema changes
Validates proposed RDF triples
Applies updates to ontology files
Supports manual approval mode
Production Deployment
Docker Deployment
# Build image
docker build -t ontology-mcp-self-healing .
# Run container
docker run -d \
-e ANTHROPIC_API_KEY=your_key \
-v $(pwd)/ontologies:/app/ontologies \
-v $(pwd)/logs:/app/logs \
ontology-mcp-self-healingDocker Compose
# Start all services
docker-compose up -d
# View logs
docker-compose logs -f self-healing
# Stop services
docker-compose downSee docs/deployment.md for Kubernetes, Helm, and production best practices.
Testing
# Run all tests
pytest tests/ -v
# Run with coverage
pytest tests/ --cov=src --cov-report=html
# Run specific test
pytest tests/test_mcp_server.py -vDocumentation
Architecture Guide - Detailed system architecture
Deployment Guide - Production deployment instructions
Troubleshooting - Common issues and solutions
Project Structure
ontology-mcp-self-healing/
├── README.md
├── requirements.txt
├── setup.py
├── docker-compose.yml
├── Dockerfile
├── config/
│ └── config.yaml
├── ontologies/
│ └── business_domain.owl
├── src/
│ ├── mcp_server/ # MCP server implementation
│ ├── monitoring/ # Schema monitoring
│ ├── healing/ # Auto-healing
│ ├── system/ # Orchestration
│ └── agents/ # Agent implementations
├── tests/ # Test suite
├── examples/ # Example scripts
├── scripts/ # Setup scripts
└── docs/ # DocumentationGetting Started
New to this project? Follow our comprehensive SETUP_GUIDE.md for step-by-step instructions on:
Cloning the repository
Setting up your environment
Running examples
Running tests
Troubleshooting common issues
Contributing
Contributions are welcome! Please see CONTRIBUTING.md for guidelines.
License
MIT License - see LICENSE file for details.
Publishing to GitHub
Want to publish this project? See GITHUB_SETUP.md for step-by-step instructions.
Acknowledgments
Built with owlready2 for ontology management
Uses Anthropic Claude for AI-powered healing
Implements MCP Protocol for agent communication
Powered by LangChain for agent orchestration
Related Articles
Medium Article: Self-Healing AI Agents - Deep dive into the system design
Blog Post: MCP for Production - Using MCP in production systems
Made with ❤️ for the AI agent community
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