JMeter MCP Server

""" Data models for the JMeter Test Results Analyzer. This module defines the core data structures used throughout the analyzer, including TestResults, Sample, and various metrics classes. """ from dataclasses import dataclass, field from datetime import datetime from typing import Dict, List, Optional @dataclass class Sample: """Represents a single sample/request in a JMeter test.""" timestamp: datetime label: str response_time: int # in milliseconds success: bool response_code: str error_message: Optional[str] = None thread_name: Optional[str] = None bytes_sent: Optional[int] = None bytes_received: Optional[int] = None latency: Optional[int] = None # in milliseconds connect_time: Optional[int] = None # in milliseconds @dataclass class TestResults: """Represents the results of a JMeter test.""" samples: List[Sample] = field(default_factory=list) start_time: Optional[datetime] = None end_time: Optional[datetime] = None def add_sample(self, sample: Sample) -> None: """Add a sample to the test results.""" self.samples.append(sample) # Update start and end times if self.start_time is None or sample.timestamp < self.start_time: self.start_time = sample.timestamp if self.end_time is None or sample.timestamp > self.end_time: self.end_time = sample.timestamp @dataclass class OverallMetrics: """Represents overall metrics for a test or endpoint.""" total_samples: int = 0 error_count: int = 0 error_rate: float = 0.0 average_response_time: float = 0.0 median_response_time: float = 0.0 percentile_90: float = 0.0 percentile_95: float = 0.0 percentile_99: float = 0.0 min_response_time: float = 0.0 max_response_time: float = 0.0 throughput: float = 0.0 # requests per second test_duration: float = 0.0 # in seconds @dataclass class EndpointMetrics(OverallMetrics): """Represents metrics for a specific endpoint/sampler.""" endpoint: str = "" @dataclass class TimeSeriesMetrics: """Represents metrics for a specific time interval.""" timestamp: datetime active_threads: int = 0 throughput: float = 0.0 average_response_time: float = 0.0 error_rate: float = 0.0 @dataclass class Bottleneck: """Represents a performance bottleneck.""" endpoint: str metric_type: str # response_time, error_rate, etc. value: float threshold: float severity: str # high, medium, low @dataclass class Anomaly: """Represents a performance anomaly.""" timestamp: datetime endpoint: str expected_value: float actual_value: float deviation_percentage: float @dataclass class Recommendation: """Represents a performance improvement recommendation.""" issue: str recommendation: str expected_impact: str implementation_difficulty: str # high, medium, low @dataclass class Insight: """Represents a performance insight.""" topic: str description: str supporting_data: Dict = field(default_factory=dict)