# Grafana Loki MCP - Architecture Patterns & Design Decisions
## Core Architecture Pattern: Layered Service Architecture
```
┌─────────────────────────┐
│ MCP Tools Layer │ ← @mcp.tool() decorators, FastMCP framework
├─────────────────────────┤
│ Business Logic Layer │ ← GrafanaClient, time parsing, error handling
├─────────────────────────┤
│ HTTP Client Layer │ ← requests library, connection management
├─────────────────────────┤
│ Grafana API │ ← External Grafana/Loki services
└─────────────────────────┘
```
## Key Design Patterns Identified
### 1. Client-Server Abstraction Pattern
- **GrafanaClient**: Encapsulates all Grafana API interactions
- **Clean Interface**: Simple method signatures hiding HTTP complexity
- **Error Translation**: HTTP errors → domain-specific exceptions
### 2. Configuration Strategy Pattern
- **Environment Variables**: Primary configuration source
- **Command Line Args**: Override mechanism via argparse
- **Sensible Defaults**: Fallback values for optional settings
### 3. Time Format Adapter Pattern
- **Multiple Input Formats**: Grafana relative, ISO8601, Unix timestamps
- **Unified Output**: All formats → Unix nanoseconds for Loki
- **Graceful Degradation**: Invalid formats → current time
### 4. Lazy Initialization Pattern
- **Datasource Discovery**: UID cached after first lookup
- **Description Generation**: Dynamic tool descriptions on server start
- **Client Creation**: Instantiated only when needed
## SOLID Principles Adherence
### Single Responsibility Principle ✅
- **GrafanaClient**: Only Grafana API interactions
- **parse_grafana_time()**: Only time format conversion
- **MCP tools**: Each tool has single, focused purpose
### Open/Closed Principle ✅
- **Extensible**: New MCP tools easily added via decorators
- **Configurable**: Behavior modification through environment variables
- **Plugin-ready**: MCP framework supports additional capabilities
### Liskov Substitution Principle ✅
- **Interface Consistency**: All MCP tools follow same pattern
- **Error Handling**: Consistent exception behavior across methods
### Interface Segregation Principle ✅
- **Focused Interfaces**: Each MCP tool exposes only necessary parameters
- **Optional Parameters**: Non-required functionality clearly separated
### Dependency Inversion Principle ✅
- **Abstraction Layers**: Business logic depends on interfaces, not implementations
- **HTTP Library**: Could swap requests for httpx without business logic changes
## Error Handling Strategy
### 3-Layer Error Handling
1. **HTTP Layer**: requests.exceptions.RequestException
2. **Translation Layer**: Convert to ValueError with context
3. **MCP Layer**: Framework handles final error serialization
### Error Context Preservation
```python
# Pattern: Enrich error messages with response details
try:
error_json = e.response.json()
error_detail = f"{error_detail} - Details: {json.dumps(error_json)}"
except Exception:
if e.response.text:
error_detail = f"{error_detail} - Response: {e.response.text}"
```
## Testing Architecture
### Test Strategy: Comprehensive Mocking
- **Session-scoped fixtures**: Prevent external API calls during tests
- **Mock Grafana responses**: Controlled test environment
- **Edge case coverage**: Invalid inputs, network failures, malformed responses
### Test Organization
- **Unit Tests**: Individual function behavior
- **Integration Tests**: End-to-end MCP tool functionality
- **Time Parsing Tests**: Comprehensive format validation
## Performance Considerations
### Optimization Strategies
1. **Connection Reuse**: requests library handles connection pooling
2. **Lazy Loading**: Datasource UID cached to avoid repeated lookups
3. **Result Limiting**: Configurable log line limits prevent memory issues
4. **String Truncation**: max_per_line parameter controls memory usage
### Scalability Patterns
- **Stateless Design**: No shared state between requests
- **Resource Limits**: Built-in limits prevent resource exhaustion
- **Graceful Degradation**: Fallback behaviors for edge cases
## Future Architecture Considerations
### Enhancement Opportunities
1. **Caching Layer**: Redis/memory cache for datasource metadata
2. **Rate Limiting**: Token bucket pattern for API calls
3. **Observability**: Structured logging with correlation IDs
4. **Connection Pooling**: Custom pool configuration for high throughput
### Extensibility Points
- **Additional MCP Tools**: New Loki/Grafana endpoints
- **Format Support**: Additional time formats or output formats
- **Authentication**: Support for other Grafana auth methods
- **Multi-tenant**: Support for multiple Grafana instances
## Code Quality Patterns
### Type Safety Strategy
- **Comprehensive Type Hints**: All functions properly annotated
- **Generic Types**: Dict[str, Any] for JSON responses
- **Optional Parameters**: Proper Optional[] usage
- **Cast Operations**: Safe type assertions with cast()
### Documentation Strategy
- **Function Docstrings**: Args, returns, exceptions documented
- **Inline Comments**: Minimal but strategic placement
- **Example Usage**: examples/ directory with working code
This architecture represents a well-designed, production-ready implementation following Python and software engineering best practices.
