Environment Analysis Engine

Overview

The Environment Analysis Engine (EnvironmentAnalyzer) is a sophisticated detection component that identifies MCP (Model Context Protocol) servers through comprehensive analysis of environment variables in both system-wide and process-specific contexts. This detection method is crucial for discovering MCP servers that rely on environment-based configuration, which is a common deployment pattern in containerized and cloud-native environments.

Architecture

Class Hierarchy

classDiagram
    class MCPDetector {
        <<abstract>>
        +settings
        +logger
        +get_detection_method()* DetectionMethod
        +detect(target_host, **kwargs)* DetectionResult
    }
    
    class EnvironmentAnalyzer {
        +mcp_env_patterns: List[str]
        +known_mcp_env_vars: Set[str]
        +mcp_value_patterns: List[str]
        +port_patterns: List[str]
        +common_mcp_ports: List[int]
        +transport_indicators: Dict[TransportType, List[str]]
        +detect(target_host, **kwargs) DetectionResult
        +_analyze_system_environment() Dict[str, str]
        +_analyze_process_environments() List[Dict]
        +_is_mcp_related_env_var(key, value) bool
        +_extract_servers_from_env() List[MCPServerInfo]
        +_extract_host_from_env() Optional[str]
        +_extract_port_from_env() Optional[int]
        +_extract_transport_from_env() TransportType
        +_determine_server_type_from_env() MCPServerType
        +_extract_env_indicators() Dict[str, Any]
        +_calculate_process_env_confidence() float
        +_extract_mcp_indicators() List[Dict]
        +_extract_transport_indicators() List[Dict]
        +_extract_port_indicators() List[Dict]
        +_extract_security_indicators() List[Dict]
        +_get_env_var_confidence() float
        +_select_best_mcp_server() MCPServerInfo
    }
    
    MCPDetector <|-- EnvironmentAnalyzer
    
    class DetectionResult {
        +target_host: str
        +detection_method: DetectionMethod
        +success: bool
        +mcp_server: MCPServerInfo
        +confidence: float
        +raw_data: Dict
        +scan_duration: float
        +error: str
    }
    
    class MCPServerInfo {
        +host: str
        +server_type: MCPServerType
        +transport_type: TransportType
        +environment_info: Dict
        +port: int
    }
    
    EnvironmentAnalyzer --> DetectionResult
    DetectionResult --> MCPServerInfo

Detection Method Integration

The EnvironmentAnalyzer integrates with the detection pipeline as follows:

sequenceDiagram
    participant Pipeline as DetectionPipeline
    participant Env as EnvironmentAnalyzer
    participant System as SystemEnv
    participant Process as ProcessEnv
    participant Result as DetectionResult
    
    Pipeline->>Env: detect(target_host, **kwargs)
    Env->>Env: Initialize detection data structure
    
    par System Environment Analysis
        Env->>System: _analyze_system_environment()
        System-->>Env: system_env_vars{}
    and Process Environment Analysis
        Env->>Process: _analyze_process_environments()
        Process-->>Env: process_env_vars[]
    end
    
    Env->>Env: _extract_servers_from_env(system_env)
    Env->>Env: _extract_servers_from_env(process_envs)
    
    Env->>Env: _extract_mcp_indicators(data)
    Env->>Env: _extract_transport_indicators(data)
    Env->>Env: _extract_port_indicators(data)
    Env->>Env: _extract_security_indicators(data)
    
    Env->>Env: _select_best_mcp_server(servers)
    Env->>Env: _calculate_confidence(server, data)
    
    Env->>Result: Create DetectionResult
    Env-->>Pipeline: Return DetectionResult

Detection Strategies

1. System Environment Analysis

Purpose: Analyze system-wide environment variables for MCP server indicators.

Algorithm:

Iterate through all system environment variables (os.environ)
Apply MCP identification patterns to each key-value pair
Collect variables that match MCP patterns
Extract server configuration from matching variables

Key Components:

Pattern Matching: Applies regex patterns to identify MCP-related variables
Configuration Extraction: Extracts host, port, transport, and other configuration details
Security Analysis: Identifies authentication and security-related configurations

Example Detection Flow:

# Analyze system environment
for key, value in os.environ.items():
    if self._is_mcp_related_env_var(key, value):
        env_vars[key] = value

# Extract server information
servers = self._extract_servers_from_env(env_vars, 'system')

2. Process Environment Analysis

Purpose: Analyze environment variables of individual running processes for MCP indicators.

Algorithm:

Use psutil to enumerate all running processes
Extract environment variables for each process
Filter processes with MCP-related environment variables
Analyze process-specific MCP configurations
Calculate confidence scores for each process

Process Filtering:

Skip system processes (PID < 100)
Handle access permission errors gracefully
Only include processes with MCP-related environment variables

Example Detection Flow:

# Iterate through all processes
for proc in psutil.process_iter(['pid', 'name', 'cmdline']):
    try:
        env_dict = proc.environ()
        
        # Filter for MCP-related variables
        mcp_env_vars = {}
        for key, value in env_dict.items():
            if self._is_mcp_related_env_var(key, value):
                mcp_env_vars[key] = value
        
        # Analyze if MCP-related variables found
        if mcp_env_vars:
            # Process and analyze
    except psutil.AccessDenied:
        continue  # Skip inaccessible processes

Exception Handling:

psutil.AccessDenied: Insufficient permissions to access process environment
psutil.NoSuchProcess: Process terminated during enumeration
psutil.ZombieProcess: Process in zombie state
ImportError: psutil module not available

Environment Variable Pattern System

MCP Environment Variable Patterns

The system uses comprehensive regex patterns to identify MCP-related variables:

mcp_env_patterns = [
    # Direct MCP patterns
    r'MCP_.*',                    # Standard MCP_ prefix
    r'.*_MCP_.*',                 # MCP in middle of variable name
    r'MODEL_CONTEXT_PROTOCOL_.*', # Full protocol name
    r'.*MCP.*SERVER.*',           # MCP server combinations
    r'.*MCP.*PORT.*',             # MCP port variables
    r'.*MCP.*HOST.*',             # MCP host variables
    r'.*MCP.*CONFIG.*',           # MCP configuration variables
    
    # Transport-specific patterns
    r'.*MCP.*HTTP.*',             # HTTP transport
    r'.*MCP.*WEBSOCKET.*',        # WebSocket transport
    r'.*MCP.*WS.*',               # WebSocket shorthand
    r'.*MCP.*STDIO.*',            # STDIO transport
    
    # Tool and capability patterns
    r'.*MCP.*TOOL.*',             # Tool-related variables
    r'.*MCP.*CAPABILITY.*',       # Capability configurations
    r'.*MCP.*RESOURCE.*',         # Resource configurations
    r'.*MCP.*PROMPT.*',           # Prompt-related variables
    
    # Authentication patterns
    r'.*MCP.*AUTH.*',             # Authentication variables
    r'.*MCP.*TOKEN.*',            # Token-based auth
    r'.*MCP.*KEY.*',              # API key variables
    r'.*MCP.*SECRET.*',           # Secret variables
]

Known MCP Environment Variables

Maintains a curated list of standard MCP environment variables:

known_mcp_env_vars = {
    'MCP_SERVER_PORT',           # Server port configuration
    'MCP_SERVER_HOST',           # Server host configuration
    'MCP_SERVER_CONFIG',         # Server configuration file
    'MCP_TRANSPORT_TYPE',        # Transport type specification
    'MCP_WEBSOCKET_PORT',        # WebSocket-specific port
    'MCP_HTTP_PORT',             # HTTP-specific port
    'MCP_STDIO_MODE',            # STDIO mode configuration
    'MCP_AUTH_TOKEN',            # Authentication token
    'MCP_API_KEY',               # API key for authentication
    'MCP_SERVER_NAME',           # Server identification
    'MCP_CAPABILITIES',          # Enabled capabilities
    'MCP_TOOLS_CONFIG',          # Tools configuration
    'MCP_RESOURCES_PATH',        # Resources directory path
    'MCP_PROMPTS_PATH',          # Prompts directory path
    'MCP_LOG_LEVEL',             # Logging level
    'MCP_DEBUG',                 # Debug mode flag
    'MODEL_CONTEXT_PROTOCOL_PORT', # Alternative port variable
    'MODEL_CONTEXT_PROTOCOL_HOST', # Alternative host variable
}

MCP Value Patterns

Identifies MCP-related content in environment variable values:

mcp_value_patterns = [
    r'.*@modelcontextprotocol.*',     # Official npm packages
    r'.*mcp-server.*',                # MCP server executables
    r'.*model-context-protocol.*',    # Protocol references
    r'.*mcp\..*',                     # MCP domains/configs
    r'.*://.*mcp.*',                  # MCP URLs
    r'.*mcp://.*',                    # MCP protocol URLs
]

Configuration Extraction Algorithms

Host Extraction

Algorithm: Hierarchical search for host configuration

def _extract_host_from_env(self, env_vars: Dict[str, str]) -> Optional[str]:
    # Priority order for host variables
    host_vars = ['MCP_SERVER_HOST', 'MCP_HOST', 'HOST', 'SERVER_HOST']
    
    # Check explicit host variables first
    for var in host_vars:
        if var in env_vars:
            return env_vars[var]
    
    # Check for host in other variables
    for key, value in env_vars.items():
        if 'host' in key.lower() and value:
            return value
    
    return None  # Default to None if no host found

Extraction Priority:

MCP_SERVER_HOST: Primary MCP host variable
MCP_HOST: Alternative MCP host variable
HOST: Generic host variable
SERVER_HOST: Server-specific host variable
Pattern Match: Any variable containing "host" in the name

Port Extraction

Algorithm: Multi-pattern port extraction with validation

def _extract_port_from_env(self, env_vars: Dict[str, str]) -> Optional[int]:
    # Check explicit port variables
    port_vars = ['MCP_SERVER_PORT', 'MCP_PORT', 'PORT', 'SERVER_PORT']
    
    for var in port_vars:
        if var in env_vars:
            try:
                return int(env_vars[var])
            except ValueError:
                continue
    
    # Check for port patterns in values
    for key, value in env_vars.items():
        for pattern in self.port_patterns:
            match = re.search(pattern, value, re.IGNORECASE)
            if match:
                try:
                    port = int(match.group(1))
                    if 1 <= port <= 65535:  # Valid port range
                        return port
                except (ValueError, IndexError):
                    continue
    
    return None

Port Patterns:

port_patterns = [
    r'PORT[=:](\d+)',           # PORT=3000 or PORT:3000
    r'.*_PORT[=:](\d+)',        # ANY_PORT=3000
    r'.*PORT.*[=:](\d+)',       # Any port variable
    r'://[^:]+:(\d+)',          # URL format: http://host:port
    r':(\d+)/',                 # Port in URL path: :3000/
    r':(\d+)$',                 # Port at end: :3000
    r'(\d+)',                   # Fallback numeric values
]

Common MCP Ports: [3000, 8000, 8080, 9000, 3001, 8001]

Transport Type Inference

Algorithm: Multi-level transport type detection

def _extract_transport_from_env(self, env_vars: Dict[str, str]) -> TransportType:
    # Check explicit transport variables
    transport_vars = ['MCP_TRANSPORT_TYPE', 'MCP_TRANSPORT', 'TRANSPORT_TYPE']
    
    for var in transport_vars:
        if var in env_vars:
            transport_value = env_vars[var].lower()
            if 'websocket' in transport_value or 'ws' in transport_value:
                return TransportType.WEBSOCKET
            elif 'http' in transport_value:
                return TransportType.HTTP
            elif 'stdio' in transport_value:
                return TransportType.STDIO
    
    # Infer from all environment variable values
    all_values = ' '.join(env_vars.values()).lower()
    
    for transport_type, indicators in self.transport_indicators.items():
        for indicator in indicators:
            if indicator in all_values:
                return transport_type
    
    # Default based on port presence
    if self._extract_port_from_env(env_vars):
        return TransportType.HTTP  # Port suggests network transport
    else:
        return TransportType.STDIO  # Default to STDIO

Transport Indicators:

transport_indicators = {
    TransportType.HTTP: [
        'http', 'https', 'rest', 'api', 'web'
    ],
    TransportType.WEBSOCKET: [
        'websocket', 'ws', 'wss', 'socket'
    ],
    TransportType.STDIO: [
        'stdio', 'stdin', 'stdout', 'pipe', 'cli'
    ]
}

Server Type Determination

Algorithm: Context-based server type classification

def _determine_server_type_from_env(self, env_vars: Dict[str, str], source: str) -> MCPServerType:
    all_values = ' '.join(env_vars.values()).lower()
    
    if 'docker' in source or 'container' in all_values:
        return MCPServerType.DOCKER_CONTAINER
    elif 'npx' in all_values or '@modelcontextprotocol' in all_values:
        return MCPServerType.NPX_PACKAGE
    elif 'node' in all_values or 'npm' in all_values:
        return MCPServerType.STANDALONE  # Node.js applications
    else:
        return MCPServerType.STANDALONE  # Default type

Classification Logic:

Docker Container: Docker-related indicators in source or values
NPX Package: NPX or official npm package indicators
Standalone: Node.js indicators or default classification

Indicator Extraction System

Environment Variable Categorization

Algorithm: Multi-category indicator extraction

def _extract_env_indicators(self, env_vars: Dict[str, str]) -> Dict[str, Any]:
    indicators = {
        'mcp_vars': [],        # MCP-specific variables
        'transport_vars': [],  # Transport configuration variables
        'port_vars': [],       # Port configuration variables
        'auth_vars': [],       # Authentication variables
        'config_vars': [],     # Configuration variables
        'tool_vars': [],       # Tool and capability variables
    }
    
    for key, value in env_vars.items():
        key_lower = key.lower()
        
        # Categorize based on key patterns
        if any(pattern in key_lower for pattern in ['mcp', 'model_context_protocol']):
            indicators['mcp_vars'].append({'key': key, 'value': value})
        
        if any(pattern in key_lower for pattern in ['transport', 'http', 'websocket', 'stdio']):
            indicators['transport_vars'].append({'key': key, 'value': value})
        
        # ... additional categorization logic
    
    return indicators

Indicator Categories:

MCP Variables: Core MCP protocol variables
Transport Variables: Communication protocol configurations
Port Variables: Network port configurations
Authentication Variables: Security and authentication settings
Configuration Variables: General configuration settings
Tool Variables: Tool and capability configurations

MCP Indicators

Purpose: Extract and classify MCP-specific environment variables

def _extract_mcp_indicators(self, detection_data: Dict[str, Any]) -> List[Dict[str, Any]]:
    indicators = []
    
    # System environment indicators
    for key, value in detection_data.get('system_env_vars', {}).items():
        indicators.append({
            'source': 'system',
            'type': 'environment_variable',
            'key': key,
            'value': value,
            'confidence': self._get_env_var_confidence(key, value)
        })
    
    # Process environment indicators
    for proc_env in detection_data.get('process_env_vars', []):
        for key, value in proc_env.get('env_vars', {}).items():
            indicators.append({
                'source': f"process_{proc_env['pid']}",
                'type': 'environment_variable',
                'key': key,
                'value': value,
                'process_name': proc_env['name'],
                'confidence': self._get_env_var_confidence(key, value)
            })
    
    return indicators

Transport Indicators

Purpose: Identify transport-related configuration across all environment variables

def _extract_transport_indicators(self, detection_data: Dict[str, Any]) -> List[Dict[str, Any]]:
    indicators = []
    
    # Aggregate all environment variables
    all_env_vars = {}
    all_env_vars.update(detection_data.get('system_env_vars', {}))
    
    for proc_env in detection_data.get('process_env_vars', []):
        all_env_vars.update(proc_env.get('env_vars', {}))
    
    # Check for transport indicators
    for key, value in all_env_vars.items():
        for transport_type, transport_indicators in self.transport_indicators.items():
            for indicator in transport_indicators:
                if indicator in key.lower() or indicator in value.lower():
                    indicators.append({
                        'transport_type': transport_type.value,
                        'indicator': indicator,
                        'env_var': key,
                        'value': value
                    })
    
    return indicators

Port Indicators

Purpose: Extract and validate port configurations from environment variables

def _extract_port_indicators(self, detection_data: Dict[str, Any]) -> List[Dict[str, Any]]:
    indicators = []
    
    # Check all environment variables for port patterns
    for key, value in all_env_vars.items():
        if 'port' in key.lower():
            for pattern in self.port_patterns:
                match = re.search(pattern, value, re.IGNORECASE)
                if match:
                    try:
                        port = int(match.group(1))
                        if 1 <= port <= 65535:  # Valid port range
                            indicators.append({
                                'port': port,
                                'env_var': key,
                                'value': value,
                                'is_common_mcp_port': port in self.common_mcp_ports
                            })
                    except (ValueError, IndexError):
                        continue
    
    return indicators

Security Indicators

Purpose: Identify security and authentication-related configurations

def _extract_security_indicators(self, detection_data: Dict[str, Any]) -> List[Dict[str, Any]]:
    indicators = []
    
    security_patterns = [
        ('auth', 'authentication'),         # Authentication patterns
        ('token', 'token_authentication'),  # Token-based auth
        ('key', 'api_key'),                 # API key authentication
        ('secret', 'secret_key'),           # Secret key patterns
        ('password', 'password'),           # Password authentication
        ('ssl', 'ssl_tls'),                 # SSL/TLS security
        ('tls', 'ssl_tls'),                 # TLS security
        ('https', 'https_transport'),       # HTTPS transport
        ('wss', 'websocket_secure'),        # Secure WebSocket
    ]
    
    for key, value in all_env_vars.items():
        key_lower = key.lower()
        value_lower = value.lower()
        
        for pattern, security_type in security_patterns:
            if pattern in key_lower or pattern in value_lower:
                indicators.append({
                    'security_type': security_type,
                    'env_var': key,
                    'value': value,
                    'has_value': bool(value.strip())  # Check if value is present
                })
    
    return indicators

Confidence Scoring Algorithms

Environment Variable Confidence

Algorithm: Hierarchical confidence scoring for individual environment variables

def _get_env_var_confidence(self, key: str, value: str) -> float:
    confidence = 0.0
    
    # High confidence for known MCP variables
    if key.upper() in self.known_mcp_env_vars:
        confidence += 0.6
    
    # Medium confidence for MCP patterns in key
    for pattern in self.mcp_env_patterns:
        if re.search(pattern, key, re.IGNORECASE):
            confidence += 0.4
            break
    
    # Low confidence for MCP patterns in value
    for pattern in self.mcp_value_patterns:
        if re.search(pattern, value, re.IGNORECASE):
            confidence += 0.2
            break
    
    # Bonus for having a meaningful value
    if confidence > 0 and value and value.strip():
        confidence += 0.1
    
    return min(confidence, 1.0)

Confidence Levels:

0.6: Known official MCP environment variables
0.4: Pattern matches in variable names
0.2: Pattern matches in variable values
0.1: Bonus for non-empty values

Process Environment Confidence

Algorithm: Composite confidence scoring for process-level analysis

def _calculate_process_env_confidence(self, env_vars: Dict[str, str], cmdline: List[str]) -> float:
    confidence = 0.0
    
    # Base confidence for having MCP environment variables
    confidence += 0.3
    
    # Bonus for known MCP environment variables
    known_vars = sum(1 for key in env_vars.keys() if key.upper() in self.known_mcp_env_vars)
    confidence += min(known_vars * 0.1, 0.3)  # Max 0.3 bonus
    
    # Bonus for MCP-related command line
    cmdline_str = ' '.join(cmdline).lower()
    mcp_patterns = ['mcp', 'model-context-protocol', '@modelcontextprotocol']
    if any(pattern in cmdline_str for pattern in mcp_patterns):
        confidence += 0.2
    
    # Bonus for transport configuration
    if any('transport' in key.lower() for key in env_vars.keys()):
        confidence += 0.1
    
    # Bonus for port configuration
    if any('port' in key.lower() for key in env_vars.keys()):
        confidence += 0.1
    
    return min(confidence, 1.0)

Scoring Components:

0.3: Base confidence for MCP environment variables
0.1 per known variable: Up to 0.3 total for known MCP variables
0.2: MCP indicators in command line
0.1: Transport configuration present
0.1: Port configuration present

Overall Detection Confidence

Algorithm: Weighted confidence calculation combining multiple evidence sources

def _calculate_confidence(self, server: MCPServerInfo, detection_data: Dict[str, Any]) -> float:
    confidence = 0.0
    
    # Base confidence from having a server
    confidence += 0.2
    
    # Confidence from MCP indicators
    mcp_indicators = detection_data.get('mcp_indicators', [])
    if mcp_indicators:
        avg_indicator_confidence = sum(
            indicator.get('confidence', 0) 
            for indicator in mcp_indicators
        ) / len(mcp_indicators)
        confidence += avg_indicator_confidence * 0.4
    
    # Bonus for transport indicators
    if detection_data.get('transport_indicators'):
        confidence += 0.1
    
    # Bonus for port indicators
    port_indicators = detection_data.get('port_indicators', [])
    if port_indicators:
        confidence += 0.1
        # Extra bonus for common MCP ports
        if any(indicator.get('is_common_mcp_port') for indicator in port_indicators):
            confidence += 0.1
    
    # Bonus for security indicators
    if detection_data.get('security_indicators'):
        confidence += 0.1
    
    return min(confidence, 1.0)

Server Selection Logic

Best Server Selection

Algorithm: Confidence-based server selection with weighted scoring

def _select_best_mcp_server(self, servers: List[MCPServerInfo]) -> Optional[MCPServerInfo]:
    if not servers:
        return None
    
    # Calculate confidence for each server
    servers_with_confidence = []
    for server in servers:
        env_info = server.environment_info or {}
        env_vars = env_info.get('env_vars', {})
        
        # Average confidence of all environment variables
        confidence = sum(
            self._get_env_var_confidence(k, v) 
            for k, v in env_vars.items()
        ) / max(len(env_vars), 1)
        
        servers_with_confidence.append((server, confidence))
    
    # Sort by confidence (highest first) and return best
    servers_with_confidence.sort(key=lambda x: x[1], reverse=True)
    return servers_with_confidence[0][0]

Selection Criteria:

Confidence Score: Average confidence of all environment variables
Variable Count: More environment variables indicate higher likelihood
Variable Quality: Known MCP variables weighted higher than pattern matches

Server Analysis Pipeline

graph TD
    A[Environment Variables] --> B{System or Process?}
    B -->|System| C[System Environment Analysis]
    B -->|Process| D[Process Environment Analysis]
    
    C --> E[Extract Server Configuration]
    D --> E
    
    E --> F[Determine Host]
    F --> G[Extract Port]
    G --> H[Infer Transport Type]
    H --> I[Classify Server Type]
    I --> J[Create MCPServerInfo]
    
    J --> K{Multiple Servers?}
    K -->|Yes| L[Calculate Confidence Scores]
    K -->|No| M[Return Single Server]
    
    L --> N[Select Highest Confidence]
    N --> M

Security Context Analysis

Authentication Detection

Purpose: Identify authentication mechanisms in environment variables

Authentication Patterns:

Token-based: *TOKEN*, *AUTH* patterns
API Key: *KEY*, *SECRET* patterns
Password: *PASSWORD*, *PASS* patterns
Certificate: *CERT*, *SSL*, *TLS* patterns

Security Risk Assessment:

def _assess_security_risks(self, env_vars: Dict[str, str]) -> List[Dict[str, Any]]:
    risks = []
    
    for key, value in env_vars.items():
        key_lower = key.lower()
        
        # Check for exposed secrets
        if any(pattern in key_lower for pattern in ['secret', 'password', 'key']):
            if value and len(value) > 0:
                risks.append({
                    'type': 'exposed_secret',
                    'variable': key,
                    'severity': 'high',
                    'description': 'Potentially sensitive information in environment variable'
                })
        
        # Check for insecure protocols
        if 'http://' in value.lower() and not 'https://' in value.lower():
            risks.append({
                'type': 'insecure_transport',
                'variable': key,
                'severity': 'medium',
                'description': 'Insecure HTTP transport detected'
            })
    
    return risks

Configuration Security

Secure Configuration Indicators:

HTTPS Transport: https:// URLs in values
Secure WebSocket: wss:// URLs in values
TLS Configuration: SSL/TLS certificate paths
Authentication Tokens: Presence of auth tokens (without values)

Error Handling and Resilience

Exception Categories

System Environment Errors:

PermissionError: Insufficient permissions to read environment
OSError: System-level errors accessing environment

Process Environment Errors:

ImportError: psutil module not available
psutil.AccessDenied: Insufficient permissions to access process
psutil.NoSuchProcess: Process terminated during enumeration
psutil.ZombieProcess: Process in zombie state

Analysis Errors:

ValueError: Invalid port numbers or configuration values
KeyError: Missing expected environment variable structure
AttributeError: Malformed server information objects

Graceful Degradation

# Example error handling pattern
def _analyze_process_environments(self, include_inherited: bool = False) -> List[Dict[str, Any]]:
    process_envs = []
    
    try:
        import psutil
        
        for proc in psutil.process_iter(['pid', 'name', 'cmdline']):
            try:
                # Attempt to get process environment
                env_dict = proc.environ()
                # Process environment variables...
            except (psutil.AccessDenied, psutil.NoSuchProcess, psutil.ZombieProcess):
                continue  # Skip inaccessible processes
    
    except ImportError:
        self.logger.warning("psutil not available for process environment analysis")
        return []  # Return empty list if psutil unavailable
    except Exception as e:
        self.logger.warning(f"Failed to analyze process environments: {e}")
        return []
    
    return process_envs

Performance Characteristics

Time Complexity

System Environment Analysis: O(n) where n = number of system environment variables Process Environment Analysis: O(p × m) where p = number of processes, m = average environment variables per process Pattern Matching: O(k × v) where k = number of patterns, v = number of variable names/values

Resource Usage

Memory: Moderate - stores environment variable snapshots and analysis results CPU: Medium - regex pattern matching and process enumeration I/O: Low - primarily in-memory operations with some system calls Permissions: Variable - depends on process access permissions

Optimization Features

Early Termination: Stops processing when high-confidence servers found Pattern Compilation: Regex patterns compiled once during initialization Process Filtering: Skip system processes and inaccessible processes Selective Analysis: Configurable system vs. process analysis

Integration with Detection Pipeline

Pipeline Configuration

# Enable environment analysis in pipeline
config = PipelineConfig(
    enable_environment_analysis=True,
    # Environment-specific options
    analyze_system=True,
    analyze_processes=True,
    include_inherited=False,
)

# Pipeline initializes EnvironmentAnalyzer
if config.enable_environment_analysis:
    detectors[DetectionMethod.ENVIRONMENT_ANALYSIS] = EnvironmentAnalyzer(settings)

Detection Orchestration

The pipeline coordinates environment analysis with other methods:

Parallel Execution: Environment analysis runs alongside other detection methods
Result Correlation: Environment data correlated with process and configuration findings
Confidence Integration: Environment confidence scores integrated into overall assessment
Context Enhancement: Environment data provides additional context for other detections

Result Integration

# Environment analysis contributes to pipeline results
pipeline_result = PipelineResult(
    target_host=target_host,
    detection_results=[
        # ... other detection results
        environment_detection_result,  # EnvironmentAnalyzer result
    ],
    best_server=selected_server,
    aggregated_confidence=weighted_confidence,
    environment_context=environment_indicators
)

Security Considerations

Information Disclosure Prevention

Risk: Exposure of sensitive environment variable values in logs or results Mitigation:

Log only variable names, not values, for sensitive variables
Implement value masking for authentication tokens and secrets
Provide configurable logging levels for sensitive information

Example Implementation:

def _log_safe_env_var(self, key: str, value: str) -> str:
    """Log environment variable safely, masking sensitive values."""
    sensitive_patterns = ['secret', 'password', 'token', 'key']
    
    if any(pattern in key.lower() for pattern in sensitive_patterns):
        return f"{key}=<MASKED>"
    else:
        return f"{key}={value}"

Access Control

Risk: Unauthorized access to process environment variables Mitigation:

Graceful handling of permission denied errors
Skip processes that cannot be accessed
Log security-related access failures for monitoring

Data Retention

Risk: Long-term storage of sensitive environment data Mitigation:

Minimal data retention in detection results
Automatic cleanup of environment variable snapshots
Configurable data retention policies

Testing Strategy

Unit Test Coverage

Pattern Matching Tests:

Known environment variable identification
Regex pattern validation
Value pattern matching accuracy

Configuration Extraction Tests:

Host extraction from various formats
Port parsing and validation
Transport type inference accuracy

Confidence Scoring Tests:

Individual variable confidence calculation
Process confidence aggregation
Overall detection confidence computation

Integration Tests

System Environment Mocking:

Mock os.environ for system environment testing
Test various environment variable combinations
Validate error handling for missing variables

Process Environment Mocking:

Mock psutil process enumeration
Simulate various process environment configurations
Test permission error handling

Pipeline Integration Tests:

Environment analysis integration with detection pipeline
Result correlation with other detection methods
Confidence score integration testing

Performance Tests

Scalability Testing:

Large environment variable set handling
Multiple process environment analysis
Pattern matching performance with many variables

Resource Usage Testing:

Memory consumption monitoring
CPU utilization measurement
Process enumeration efficiency

Future Enhancements

Advanced Pattern Recognition

Machine Learning: Pattern recognition for unknown MCP environment patterns Dynamic Patterns: Runtime pattern learning from successful detections Context-Aware Patterns: Environment-specific pattern adaptation

Enhanced Security Analysis

Vulnerability Detection: Identify insecure configurations automatically Compliance Checking: Check environment configurations against security standards Risk Scoring: Quantitative security risk assessment

Performance Optimizations

Caching: Cache process environment snapshots for repeated analysis Incremental Analysis: Detect environment changes and analyze only differences Parallel Processing: Concurrent process environment analysis

Integration Features

Configuration Management: Integration with configuration management systems Container Orchestration: Enhanced Docker and Kubernetes environment analysis Cloud Platform Integration: Cloud-specific environment analysis patterns

Conclusion

The Environment Analysis Engine provides comprehensive coverage of environment-based MCP server deployments through sophisticated pattern matching, multi-level confidence scoring, and robust error handling. Its dual approach of system-wide and process-specific analysis ensures thorough detection while maintaining security and performance standards. The engine's integration with the broader detection pipeline enhances overall detection accuracy and provides valuable context for MCP server identification and analysis.