# Technical Specifications - Missing Method Implementations
## Overview
This document provides detailed technical specifications for implementing the missing methods identified during real-world testing. Each specification includes interface definitions, implementation requirements, testing criteria, and integration guidelines.
## 1. AgentOrchestrator.get_status()
### Interface Definition
```python
def get_status(self) -> Dict[str, Any]:
"""
Retrieve current system health and operational metrics.
Returns:
Dict containing system status information including:
- overall_health: System health status (healthy/warning/critical)
- agent_status: Individual agent health and performance
- resource_metrics: CPU, memory, and storage utilization
- performance_metrics: Response times, throughput, error rates
- active_tasks: Currently executing tasks and their status
- recent_errors: Last 10 errors with timestamps and context
- uptime: System uptime and availability metrics
"""
```
### Implementation Requirements
#### Core Status Data Structure
```python
@dataclass
class SystemStatus:
timestamp: datetime
overall_health: HealthStatus # HEALTHY, WARNING, CRITICAL
uptime_seconds: int
version: str
# Agent Information
agents: Dict[str, AgentStatus]
active_tasks: List[TaskStatus]
# Performance Metrics
performance: PerformanceMetrics
resources: ResourceMetrics
# Error Tracking
recent_errors: List[ErrorInfo]
error_rate_last_hour: float
@dataclass
class AgentStatus:
name: str
type: AgentType # FRONTEND, BACKEND, DEVOPS, QUALITY
status: HealthStatus
last_activity: datetime
tasks_completed: int
average_response_time: float
current_load: float # 0.0 - 1.0
@dataclass
class PerformanceMetrics:
requests_per_minute: float
average_response_time: float
p95_response_time: float
success_rate: float
active_connections: int
@dataclass
class ResourceMetrics:
cpu_usage_percent: float
memory_usage_percent: float
disk_usage_percent: float
network_io_mbps: float
```
#### Implementation Strategy
```python
class AgentOrchestrator:
def __init__(self):
self.start_time = datetime.now()
self.request_metrics = RequestMetrics()
self.agent_health = AgentHealthTracker()
self.error_tracker = ErrorTracker()
def get_status(self) -> Dict[str, Any]:
try:
# Calculate uptime
uptime = (datetime.now() - self.start_time).total_seconds()
# Gather agent statuses
agent_statuses = self._collect_agent_statuses()
# Calculate performance metrics
perf_metrics = self._calculate_performance_metrics()
# Get resource utilization
resource_metrics = self._get_resource_metrics()
# Determine overall health
overall_health = self._determine_overall_health(
agent_statuses, perf_metrics, resource_metrics
)
return {
"status": "success",
"data": {
"timestamp": datetime.now().isoformat(),
"overall_health": overall_health.value,
"uptime_seconds": int(uptime),
"version": self.get_version(),
"agents": {name: agent.to_dict() for name, agent in agent_statuses.items()},
"performance": perf_metrics.to_dict(),
"resources": resource_metrics.to_dict(),
"active_tasks": self._get_active_tasks(),
"recent_errors": self.error_tracker.get_recent_errors(limit=10),
"error_rate_last_hour": self.error_tracker.get_error_rate(hours=1)
}
}
except Exception as e:
logger.error(f"Failed to get system status: {e}")
return {
"status": "error",
"error": f"Status collection failed: {str(e)}",
"timestamp": datetime.now().isoformat()
}
```
### Testing Requirements
```python
def test_get_status_success():
orchestrator = AgentOrchestrator()
status = orchestrator.get_status()
assert status["status"] == "success"
assert "data" in status
assert "timestamp" in status["data"]
assert "overall_health" in status["data"]
assert status["data"]["overall_health"] in ["healthy", "warning", "critical"]
def test_get_status_performance():
orchestrator = AgentOrchestrator()
start_time = time.time()
status = orchestrator.get_status()
duration = time.time() - start_time
assert duration < 1.0 # Must respond within 1 second
```
## 2. AgentOrchestrator.generate_architecture()
### Interface Definition
```python
def generate_architecture(
self,
project_description: str,
tech_stack: List[str],
requirements: List[str]
) -> Dict[str, Any]:
"""
Generate comprehensive software architecture recommendations.
Args:
project_description: Detailed description of the project
tech_stack: List of preferred technologies and frameworks
requirements: Functional and non-functional requirements
Returns:
Dict containing architecture recommendations including:
- architecture_type: Recommended overall architecture pattern
- components: Detailed component breakdown and responsibilities
- data_flow: Data flow diagrams and patterns
- deployment_strategy: Infrastructure and deployment recommendations
- security_considerations: Security architecture and best practices
- scalability_plan: Scaling strategies and performance considerations
- technology_choices: Detailed technology stack recommendations
- implementation_phases: Phased development approach
"""
```
### Implementation Requirements
#### Architecture Data Models
```python
@dataclass
class ArchitectureRecommendation:
project_id: str
timestamp: datetime
architecture_type: ArchitectureType
components: List[Component]
data_flows: List[DataFlow]
deployment: DeploymentStrategy
security: SecurityArchitecture
scalability: ScalabilityPlan
technology_stack: TechnologyStack
implementation_phases: List[Phase]
@dataclass
class Component:
name: str
type: ComponentType # SERVICE, DATABASE, API, UI, GATEWAY
responsibilities: List[str]
technologies: List[str]
dependencies: List[str]
scalability_requirements: ScalabilityRequirements
@dataclass
class TechnologyStack:
frontend: List[TechnologyChoice]
backend: List[TechnologyChoice]
database: List[TechnologyChoice]
infrastructure: List[TechnologyChoice]
monitoring: List[TechnologyChoice]
```
#### Implementation Strategy
```python
class AgentOrchestrator:
def __init__(self):
self.architecture_analyzer = ArchitectureAnalyzer()
self.pattern_matcher = PatternMatcher()
self.technology_advisor = TechnologyAdvisor()
def generate_architecture(self, project_description: str, tech_stack: List[str], requirements: List[str]) -> Dict[str, Any]:
try:
# Parse and analyze requirements
parsed_requirements = self.architecture_analyzer.parse_requirements(
project_description, requirements
)
# Determine architecture pattern
architecture_pattern = self.pattern_matcher.recommend_pattern(
parsed_requirements, tech_stack
)
# Generate component architecture
components = self._generate_components(
architecture_pattern, parsed_requirements
)
# Design data flows
data_flows = self._design_data_flows(components, parsed_requirements)
# Create deployment strategy
deployment = self._create_deployment_strategy(
components, parsed_requirements
)
# Generate security recommendations
security = self._generate_security_architecture(
components, parsed_requirements
)
# Plan scalability approach
scalability = self._plan_scalability(
components, parsed_requirements
)
# Finalize technology choices
final_tech_stack = self.technology_advisor.recommend_stack(
tech_stack, components, parsed_requirements
)
# Create implementation phases
phases = self._create_implementation_phases(
components, parsed_requirements
)
return {
"status": "success",
"data": {
"project_id": self._generate_project_id(),
"timestamp": datetime.now().isoformat(),
"architecture_type": architecture_pattern.value,
"components": [comp.to_dict() for comp in components],
"data_flows": [flow.to_dict() for flow in data_flows],
"deployment": deployment.to_dict(),
"security": security.to_dict(),
"scalability": scalability.to_dict(),
"technology_stack": final_tech_stack.to_dict(),
"implementation_phases": [phase.to_dict() for phase in phases],
"estimated_timeline": self._estimate_timeline(phases),
"estimated_cost": self._estimate_cost(components, deployment)
}
}
except Exception as e:
logger.error(f"Architecture generation failed: {e}")
return {
"status": "error",
"error": f"Architecture generation failed: {str(e)}",
"timestamp": datetime.now().isoformat()
}
```
### Testing Requirements
```python
def test_generate_architecture_microservices():
orchestrator = AgentOrchestrator()
result = orchestrator.generate_architecture(
project_description="Social media platform with user management, content creation, and real-time messaging",
tech_stack=["Node.js", "React", "MongoDB", "Redis"],
requirements=["Scalable to 1M users", "Real-time messaging", "High availability"]
)
assert result["status"] == "success"
assert "data" in result
assert result["data"]["architecture_type"] in ["microservices", "monolithic", "serverless"]
assert len(result["data"]["components"]) > 0
assert "security" in result["data"]
def test_generate_architecture_performance():
orchestrator = AgentOrchestrator()
start_time = time.time()
result = orchestrator.generate_architecture("Simple REST API", ["FastAPI"], ["Basic CRUD"])
duration = time.time() - start_time
assert duration < 10.0 # Must respond within 10 seconds
```
## 3. SolutionGenerator.generate_fix()
### Interface Definition
```python
def generate_fix(
self,
code: str,
error_message: str,
context: Optional[str] = None
) -> Dict[str, Any]:
"""
Generate automated code fixes using self-healing capabilities.
Args:
code: The problematic code that needs fixing
error_message: The error message encountered
context: Additional context about the code environment
Returns:
Dict containing fix recommendations including:
- fixed_code: The corrected version of the code
- explanation: Detailed explanation of what was wrong and how it was fixed
- confidence: Confidence score (0.0-1.0) in the fix accuracy
- alternative_solutions: Other possible fixes with pros/cons
- best_practices: Related best practices and recommendations
- testing_suggestions: How to test the fix
"""
```
### Implementation Requirements
#### Fix Generation Data Models
```python
@dataclass
class CodeFix:
original_code: str
fixed_code: str
error_type: ErrorType
fix_type: FixType
confidence: float
explanation: str
changes_made: List[Change]
@dataclass
class Change:
line_number: int
original_line: str
fixed_line: str
change_type: ChangeType # REPLACE, INSERT, DELETE
reason: str
@dataclass
class AlternativeSolution:
code: str
confidence: float
pros: List[str]
cons: List[str]
use_cases: List[str]
```
#### Implementation Strategy
```python
class SolutionGenerator:
def __init__(self):
self.error_analyzer = ErrorAnalyzer()
self.pattern_detector = PatternDetector()
self.code_fixer = CodeFixer()
self.syntax_validator = SyntaxValidator()
def generate_fix(self, code: str, error_message: str, context: Optional[str] = None) -> Dict[str, Any]:
try:
# Analyze the error
error_analysis = self.error_analyzer.analyze(code, error_message, context)
# Detect common patterns and issues
detected_patterns = self.pattern_detector.detect_issues(code, error_analysis)
# Generate primary fix
primary_fix = self.code_fixer.generate_primary_fix(
code, error_analysis, detected_patterns
)
# Validate the fix syntax
validation_result = self.syntax_validator.validate(primary_fix.fixed_code)
if not validation_result.is_valid:
# Try alternative approaches
primary_fix = self._generate_alternative_fix(
code, error_analysis, detected_patterns
)
# Generate alternative solutions
alternatives = self.code_fixer.generate_alternatives(
code, error_analysis, detected_patterns, exclude=primary_fix
)
# Create best practices recommendations
best_practices = self._generate_best_practices(
error_analysis, detected_patterns
)
# Generate testing suggestions
testing_suggestions = self._generate_testing_suggestions(
primary_fix, error_analysis
)
return {
"status": "success",
"data": {
"original_code": code,
"fixed_code": primary_fix.fixed_code,
"error_type": error_analysis.error_type.value,
"fix_type": primary_fix.fix_type.value,
"confidence": primary_fix.confidence,
"explanation": primary_fix.explanation,
"changes_made": [change.to_dict() for change in primary_fix.changes_made],
"alternative_solutions": [alt.to_dict() for alt in alternatives],
"best_practices": best_practices,
"testing_suggestions": testing_suggestions,
"execution_time": self._get_execution_time()
}
}
except Exception as e:
logger.error(f"Code fix generation failed: {e}")
return {
"status": "error",
"error": f"Fix generation failed: {str(e)}",
"original_code": code,
"error_message": error_message,
"timestamp": datetime.now().isoformat()
}
def _generate_best_practices(self, error_analysis: ErrorAnalysis, patterns: List[Pattern]) -> List[str]:
"""Generate relevant best practices based on the error and code patterns."""
practices = []
if error_analysis.error_type == ErrorType.NAME_ERROR:
practices.extend([
"Always define variables before using them",
"Use meaningful variable names",
"Consider using type hints for better code clarity"
])
if PatternType.MISSING_IMPORT in [p.type for p in patterns]:
practices.extend([
"Organize imports at the top of the file",
"Use absolute imports when possible",
"Consider using virtual environments for dependency management"
])
return practices
```
### Testing Requirements
```python
def test_generate_fix_name_error():
generator = SolutionGenerator()
result = generator.generate_fix(
code="def calculate_average(numbers): return sum(numbers) / length(numbers)",
error_message="NameError: name 'length' is not defined"
)
assert result["status"] == "success"
assert "len(numbers)" in result["data"]["fixed_code"]
assert result["data"]["confidence"] > 0.8
assert "length" not in result["data"]["fixed_code"]
def test_generate_fix_performance():
generator = SolutionGenerator()
start_time = time.time()
result = generator.generate_fix("print(undefined_var)", "NameError: name 'undefined_var' is not defined")
duration = time.time() - start_time
assert duration < 5.0 # Must respond within 5 seconds
```
## Integration Guidelines
### Error Handling Standards
```python
class MCPErrorHandler:
@staticmethod
def handle_method_error(method_name: str, error: Exception) -> Dict[str, Any]:
return {
"status": "error",
"error": f"{method_name} failed: {str(error)}",
"error_type": type(error).__name__,
"timestamp": datetime.now().isoformat(),
"recovery_suggestions": [
"Check system logs for detailed error information",
"Verify all dependencies are properly installed",
"Contact support if the issue persists"
]
}
```
### Logging Standards
```python
import logging
logger = logging.getLogger(__name__)
def log_method_execution(method_name: str, params: Dict, duration: float, success: bool):
if success:
logger.info(f"{method_name} completed successfully in {duration:.2f}s", extra={
"method": method_name,
"duration": duration,
"params": params
})
else:
logger.error(f"{method_name} failed after {duration:.2f}s", extra={
"method": method_name,
"duration": duration,
"params": params
})
```
### Performance Monitoring
```python
def monitor_performance(func):
"""Decorator to monitor method performance."""
def wrapper(*args, **kwargs):
start_time = time.time()
try:
result = func(*args, **kwargs)
duration = time.time() - start_time
log_method_execution(func.__name__, kwargs, duration, True)
return result
except Exception as e:
duration = time.time() - start_time
log_method_execution(func.__name__, kwargs, duration, False)
raise
return wrapper
```
## Implementation Checklist
### Phase 1: Core Implementation
- [ ] Implement `AgentOrchestrator.get_status()`
- [ ] Implement `AgentOrchestrator.generate_architecture()`
- [ ] Implement `SolutionGenerator.generate_fix()`
- [ ] Add comprehensive error handling
- [ ] Implement performance monitoring
### Phase 2: Testing & Validation
- [ ] Unit tests for all new methods
- [ ] Integration tests with existing tools
- [ ] Performance benchmarking
- [ ] Playwright browser testing
- [ ] Error scenario testing
### Phase 3: Documentation & Deployment
- [ ] Update API documentation
- [ ] Create user guides and examples
- [ ] Deploy to staging environment
- [ ] Conduct user acceptance testing
- [ ] Deploy to production
**Success Criteria**: All three missing methods implemented with 95%+ test coverage, sub-10 second response times, and zero critical errors in production deployment.
