MCP Testing Evaluation Framework - Technical Documentation

Comprehensive technical architecture and implementation guide for the MCP Testing Evaluation Framework

1. System Architecture Overview
2. Component Specifications
3. Data Flow Architecture
4. Module Interactions
5. Configuration Management
6. Deployment Architecture
7. Performance Optimization
8. Troubleshooting Guide

System Architecture Overview

The MCP Testing Evaluation Framework is a comprehensive Node.js-based system that combines static code analysis with runtime testing to validate MCP (Model Context Protocol) servers against Anthropic's 5 core requirements.

mermaid

graph TB
    subgraph "Client Layer"
        CLI[CLI Tool]
        WEB[Web Dashboard]
        API[REST API]
        WS[WebSocket Interface]
    end
    
    subgraph "Orchestration Layer"
        EVAL[MCPEvaluator Core]
        CONFIG[Configuration Manager]
        EVENT[Event System]
    end
    
    subgraph "Testing Layer"
        STATIC[Static Analysis Engine]
        RUNTIME[Runtime Testing Engine]
        HOOKS[Python Hooks System]
        INSPECTOR[MCP Inspector Bridge]
    end
    
    subgraph "Data Layer"
        CACHE[Results Cache]
        REPORTS[Report Generator]
        METRICS[Metrics Collector]
    end
    
    subgraph "Integration Layer"
        OBS[Observability System]
        CI[CI/CD Integration]
        EXPORT[Export System]
    end
    
    CLI --> EVAL
    WEB --> API
    API --> EVAL
    WS --> EVENT
    
    EVAL --> STATIC
    EVAL --> RUNTIME
    EVAL --> CONFIG
    
    STATIC --> HOOKS
    RUNTIME --> INSPECTOR
    
    EVAL --> CACHE
    EVAL --> REPORTS
    EVAL --> METRICS
    
    EVAL --> OBS
    EVAL --> EXPORT

Core Architecture Principles

1. Event-Driven Design: All components communicate through a centralized event system
2. Modular Architecture: Clear separation between static analysis and runtime testing
3. Pluggable Components: Easy to extend with additional test types or integrations
4. Real-Time Monitoring: WebSocket-based live updates for all operations
5. Multi-Interface Support: CLI, web dashboard, and programmatic API access

Component Specifications

1. MCPEvaluator Core (`src/evaluator.js`)

Purpose: Central orchestrator managing the complete evaluation lifecycle Key Features:

• Event-driven architecture extending Node.js EventEmitter
• Configurable test execution (static-only, runtime-only, combined)
• Automatic server configuration detection
• Comprehensive scoring and recommendation system
• Integration with observability systems

Technical Specifications:

javascript

class MCPEvaluator extends EventEmitter {
  constructor(options = {}) {
    this.serverPath = options.serverPath;
    this.config = {
      runStatic: options.runStatic !== false,
      runRuntime: options.runRuntime !== false,
      transport: options.transport || 'stdio',
      hooksPath: options.hooksPath || '.claude/hooks',
      observabilityUrl: options.observabilityUrl || 'http://localhost:3456',
      timeout: options.timeout || 30000,
      retries: options.retries || 3
    };
  }
}

Event Emission Pattern:

• evaluation:started → Evaluation initialization
• static:started → Static analysis phase begins
• hook:running → Individual hook execution
• hook:completed → Hook completion with results
• runtime:started → Runtime testing phase begins
• tool:testing → Individual tool test
• tool:passed/failed → Tool test results
• evaluation:completed → Full evaluation finished
• evaluation:failed → Evaluation error occurred

2. Inspector Bridge (`src/inspector-bridge.js`)

Purpose: Programmatic interface to MCP Inspector for automated runtime testing Key Features:

• Multi-transport support (STDIO, SSE, HTTP)
• Process lifecycle management
• Automated test sequence execution
• Response validation and error detection
• Configurable timeouts and retry logic

Technical Specifications:

javascript

class InspectorBridge extends EventEmitter {
  constructor(options = {}) {
    this.config = {
      inspectorPath: options.inspectorPath || 'npx @modelcontextprotocol/inspector',
      transport: options.transport || 'stdio',
      timeout: options.timeout || 30000,
      port: options.port || 5173,
      retries: options.retries || 3,
      bufferSize: options.bufferSize || 64 * 1024
    };
  }
}

Test Execution Pipeline:

1. Server Configuration: Create temporary config for Inspector
2. Process Spawning: Launch Inspector with specific parameters
3. Connection Establishment: Wait for server readiness
4. Test Sequence: Execute predefined test patterns
5. Result Collection: Capture and validate responses
6. Process Cleanup: Terminate Inspector gracefully

3. CLI Tool (`bin/mcp-evaluate`)

Purpose: Command-line interface for automated evaluation and CI/CD integration Features:

• Multiple output formats (text, JSON, HTML)
• Configurable fail thresholds for CI/CD
• Interactive tool testing capabilities
• Verbose logging and debug modes
• Report generation and export

Command Structure:

bash

mcp-evaluate [path] [options]
Options:
  --transport stdio
ssehttp    Transport type
  --static-only                 Run static analysis only
  --runtime-only               Run runtime testing only
  --json                       JSON output format
  --report <file>              Generate report file
  --ci                         CI/CD mode with exit codes
  --fail-threshold <score>     Minimum score for success
  --timeout <ms>               Operation timeout
  --verbose                    Enable verbose logging
  --help                       Show usage information

4. Dashboard Server (`src/dashboard/server.js`)

Purpose: Real-time web interface with WebSocket support for live monitoring Technical Architecture:

javascript

const server = express();
const httpServer = createServer(server);
const io = new Server(httpServer, {
  cors: { origin: "*" },
  transports: ['websocket', 'polling']
});
// WebSocket event handling
io.on('connection', (socket) => {
  socket.on('start-evaluation', handleEvaluationStart);
  socket.on('stop-evaluation', handleEvaluationStop);
  socket.on('get-status', handleStatusRequest);
});

API Endpoints:

• POST /api/evaluate → Start new evaluation
• GET /api/evaluation/:id → Get evaluation status
• GET /api/evaluations → List all evaluations
• DELETE /api/evaluation/:id → Cancel evaluation
• GET /api/config → Get system configuration
• POST /api/config → Update configuration

Data Flow Architecture

Evaluation Lifecycle Data Flow

mermaid

sequenceDiagram
    participant Client
    participant Evaluator
    participant StaticEngine
    participant RuntimeEngine
    participant Database
    participant Observability
    Client->>Evaluator: Start Evaluation
    Evaluator->>Database: Initialize Session
    
    par Static Analysis
        Evaluator->>StaticEngine: Run Hooks
        StaticEngine->>StaticEngine: Execute 5 Hook Scripts
        StaticEngine->>Database: Store Hook Results
        StaticEngine->>Evaluator: Return Static Results
    and Runtime Testing
        Evaluator->>RuntimeEngine: Start Inspector Tests
        RuntimeEngine->>RuntimeEngine: Tool/Resource Testing
        RuntimeEngine->>Database: Store Runtime Results
        RuntimeEngine->>Evaluator: Return Runtime Results
    end
    
    Evaluator->>Evaluator: Combine Results
    Evaluator->>Database: Store Final Score
    Evaluator->>Observability: Send Metrics
    Evaluator->>Client: Return Complete Results

Data Processing Pipeline

1. Input Processing:

- Server path validation and normalization - Configuration file discovery and parsing - Dependency verification and validation

2. Static Analysis Pipeline:

code

   Python Hooks → JSON Results → Score Calculation → Evidence Collection

3. Runtime Testing Pipeline:

code

   Inspector Launch → Tool Discovery → Test Execution → Response Validation

4. Results Combination:

code

   Static Results + Runtime Results → Weighted Scoring → Recommendations

5. Output Generation:

code

   Combined Results → Report Templates → Multiple Formats (JSON/MD/HTML)

Module Interactions

Hook System Integration

The static analysis system uses Python hooks that follow a standardized interface:

python

#!/usr/bin/env python3
"""
MCP Evaluation Hook Template
Returns: {"score": float, "evidence": list, "recommendations": list}
"""
def evaluate_requirement(project_path, config=None):
    """
    Main evaluation function
    
    Args:
        project_path: Path to MCP server project
        config: Optional configuration dictionary
        
    Returns:
        dict: Evaluation results with score (0.0-1.0), evidence, recommendations
    """
    try:
        # Implementation specific logic
        score = calculate_score(project_path)
        evidence = collect_evidence(project_path)
        recommendations = generate_recommendations(score, evidence)
        
        return {
            "score": score,
            "evidence": evidence,
            "recommendations": recommendations,
            "timestamp": datetime.now().isoformat()
        }
    except Exception as e:
        return {
            "score": 0.0,
            "evidence": [f"Error: {str(e)}"],
            "recommendations": ["Fix evaluation errors before retesting"],
            "error": True
        }

Inspector Bridge Communication

The Inspector Bridge manages complex inter-process communication:

javascript

// Inspector Process Management
async startInspector(serverConfig) {
  const configPath = await this.createTempConfig(serverConfig);
  
  this.process = spawn('npx', [
    '@modelcontextprotocol/inspector',
    '--config', configPath,
    '--transport', this.config.transport,
    '--port', this.config.port,
    '--headless'
  ]);
  
  // Setup stdio handlers
  this.process.stdout.on('data', this.handleOutput.bind(this));
  this.process.stderr.on('data', this.handleError.bind(this));
  this.process.on('exit', this.handleExit.bind(this));
  
  return this.waitForReady();
}
// Test Execution
async executeTest(test) {
  const command = {
    method: 'tools/call',
    params: {
      name: test.toolName,
      arguments: test.arguments
    }
  };
  
  return new Promise((resolve, reject) => {
    const timeout = setTimeout(() => {
      reject(new Error(Test timeout: ${test.toolName}));
    }, this.config.timeout);
    
    this.sendCommand(command)
      .then(result => {
        clearTimeout(timeout);
        resolve(this.validateResponse(result));
      })
      .catch(error => {
        clearTimeout(timeout);
        reject(error);
      });
  });
}

Configuration Management

Configuration Hierarchy

The system supports multiple configuration sources with clear precedence:

1. Command Line Arguments (highest priority)
2. Environment Variables
3. Project Configuration (mcp-evaluator.config.json)
4. User Configuration (~/.mcp-evaluator.json)
5. System Defaults (lowest priority)

Configuration Schema

json

{
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "type": "object",
  "properties": {
    "inspector": {
      "type": "object",
      "properties": {
        "transport": {
          "type": "string",
          "enum": ["stdio", "sse", "http"]
        },
        "timeout": {
          "type": "integer",
          "minimum": 1000,
          "maximum": 300000
        },
        "retries": {
          "type": "integer",
          "minimum": 0,
          "maximum": 10
        },
        "port": {
          "type": "integer",
          "minimum": 1024,
          "maximum": 65535
        }
      }
    },
    "hooks": {
      "type": "object",
      "properties": {
        "enableAll": {
          "type": "boolean"
        },
        "customPatterns": {
          "type": "array",
          "items": {
            "type": "string"
          }
        },
        "hooksPath": {
          "type": "string"
        }
      }
    },
    "scoring": {
      "type": "object",
      "properties": {
        "weights": {
          "type": "object",
          "properties": {
            "static": {
              "type": "number",
              "minimum": 0,
              "maximum": 1
            },
            "runtime": {
              "type": "number",
              "minimum": 0,
              "maximum": 1
            }
          }
        },
        "thresholds": {
          "type": "object",
          "properties": {
            "pass": {
              "type": "number",
              "minimum": 0,
              "maximum": 1
            },
            "warning": {
              "type": "number",
              "minimum": 0,
              "maximum": 1
            }
          }
        }
      }
    },
    "observability": {
      "type": "object",
      "properties": {
        "enabled": {
          "type": "boolean"
        },
        "url": {
          "type": "string",
          "format": "uri"
        },
        "apiKey": {
          "type": ["string", "null"]
        }
      }
    }
  }
}

Dynamic Configuration Loading

javascript

class ConfigManager {
  constructor() {
    this.config = {};
    this.watchers = [];
  }
  
  async loadConfiguration(projectPath) {
    const sources = [
      this.loadDefaults(),
      await this.loadUserConfig(),
      await this.loadProjectConfig(projectPath),
      this.loadEnvironmentConfig(),
      this.loadCommandLineConfig()
    ];
    
    this.config = this.mergeConfigurations(sources);
    this.validateConfiguration();
    return this.config;
  }
  
  watchConfiguration(callback) {
    // File system watchers for hot reloading
    const watcher = chokidar.watch([
      'mcp-evaluator.config.json',
      '~/.mcp-evaluator.json'
    ]);
    
    watcher.on('change', () => {
      this.loadConfiguration()
        .then(config => callback(config))
        .catch(error => console.error('Config reload error:', error));
    });
    
    this.watchers.push(watcher);
  }
}

Deployment Architecture

Local Development Setup

bash

<h1 class="text-3xl font-bold mt-12 mb-8">Development environment</h1>
git clone https://github.com/your-org/multi-agent-observability-system
cd multi-agent-observability-system/apps/mcp-evaluator
<h1 class="text-3xl font-bold mt-12 mb-8">Install dependencies</h1>
npm install
pip3 install -r requirements.txt
<h1 class="text-3xl font-bold mt-12 mb-8">Install MCP Inspector</h1>
npm install -g @modelcontextprotocol/inspector
<h1 class="text-3xl font-bold mt-12 mb-8">Setup development configuration</h1>
cp config/development.json mcp-evaluator.config.json
<h1 class="text-3xl font-bold mt-12 mb-8">Start development server</h1>
npm run dev

Production Deployment

dockerfile

<h1 class="text-3xl font-bold mt-12 mb-8">Multi-stage Docker build for production</h1>
FROM node:18-alpine AS builder
WORKDIR /app
COPY package*.json ./
RUN npm ci --only=production
FROM node:18-alpine AS runtime
<h1 class="text-3xl font-bold mt-12 mb-8">Install Python and system dependencies</h1>
RUN apk add --no-cache python3 py3-pip
<h1 class="text-3xl font-bold mt-12 mb-8">Create non-root user</h1>
RUN addgroup -g 1001 -S evaluator && \
    adduser -S evaluator -u 1001
WORKDIR /app
<h1 class="text-3xl font-bold mt-12 mb-8">Copy application code</h1>
COPY --from=builder /app/node_modules ./node_modules
COPY . .
<h1 class="text-3xl font-bold mt-12 mb-8">Install Python dependencies</h1>
RUN pip3 install -r requirements.txt
<h1 class="text-3xl font-bold mt-12 mb-8">Install MCP Inspector globally</h1>
RUN npm install -g @modelcontextprotocol/inspector
<h1 class="text-3xl font-bold mt-12 mb-8">Set permissions</h1>
RUN chown -R evaluator:evaluator /app
USER evaluator
<h1 class="text-3xl font-bold mt-12 mb-8">Health check</h1>
HEALTHCHECK --interval=30s --timeout=10s --start-period=60s \
  CMD node healthcheck.js
EXPOSE 3457
CMD ["node", "src/dashboard/server.js"]

Kubernetes Deployment

yaml

apiVersion: apps/v1
kind: Deployment
metadata:
  name: mcp-evaluator
  labels:
    app: mcp-evaluator
spec:
  replicas: 3
  selector:
    matchLabels:
      app: mcp-evaluator
  template:
    metadata:
      labels:
        app: mcp-evaluator
    spec:
      containers:
      - name: evaluator
        image: mcp-evaluator:latest
        ports:
        - containerPort: 3457
        env:
        - name: NODE_ENV
          value: "production"
        - name: MCP_OBSERVABILITY_URL
          value: "http://observability-service:3456"
        resources:
          requests:
            memory: "256Mi"
            cpu: "250m"
          limits:
            memory: "512Mi"
            cpu: "500m"
        livenessProbe:
          httpGet:
            path: /health
            port: 3457
          initialDelaySeconds: 30
          periodSeconds: 10
        readinessProbe:
          httpGet:
            path: /ready
            port: 3457
          initialDelaySeconds: 5
          periodSeconds: 5

apiVersion: v1
kind: Service
metadata:
  name: mcp-evaluator-service
spec:
  selector:
    app: mcp-evaluator
  ports:
    - protocol: TCP
      port: 80
      targetPort: 3457
  type: LoadBalancer

Performance Optimization

Execution Performance Tuning

1. Parallel Processing:

javascript

// Concurrent hook execution
async runStaticAnalysis() {
  const hooks = this.getActiveHooks();
  
  // Execute hooks in parallel with concurrency limit
  const results = await Promise.allSettled(
    hooks.map(async (hook) => {
      const semaphore = await this.semaphore.acquire();
      try {
        return await this.runHook(hook);
      } finally {
        semaphore.release();
      }
    })
  );
  
  return this.processHookResults(results);
}

2. Caching Strategy:

javascript

class ResultsCache {
  constructor(options = {}) {
    this.cache = new Map();
    this.ttl = options.ttl || 3600000; // 1 hour
    this.maxSize = options.maxSize || 1000;
  }
  
  async get(key) {
    const cached = this.cache.get(key);
    if (cached && Date.now() - cached.timestamp < this.ttl) {
      return cached.data;
    }
    this.cache.delete(key);
    return null;
  }
  
  set(key, data) {
    if (this.cache.size >= this.maxSize) {
      const firstKey = this.cache.keys().next().value;
      this.cache.delete(firstKey);
    }
    
    this.cache.set(key, {
      data,
      timestamp: Date.now()
    });
  }
}

3. Memory Management:

javascript

// Stream-based processing for large files
async processLargeProject(projectPath) {
  const stream = fs.createReadStream(path.join(projectPath, 'analysis.json'));
  const parser = new StreamingJsonParser();
  
  return new Promise((resolve, reject) => {
    let results = [];
    
    stream
      .pipe(parser)
      .on('data', (chunk) => {
        results.push(this.processChunk(chunk));
        
        // Prevent memory buildup
        if (results.length > 1000) {
          results = this.aggregateResults(results);
        }
      })
      .on('end', () => resolve(results))
      .on('error', reject);
  });
}

Database Optimization

javascript

// Connection pooling and query optimization
class DatabaseManager {
  constructor(config) {
    this.pool = mysql.createPool({
      connectionLimit: 10,
      host: config.host,
      user: config.user,
      password: config.password,
      database: config.database,
      acquireTimeout: 60000,
      timeout: 60000
    });
  }
  
  async storeEvaluationResults(data) {
    const connection = await this.pool.getConnection();
    
    try {
      await connection.beginTransaction();
      
      // Batch insert for performance
      const evaluationId = await this.insertEvaluation(connection, data.meta);
      
      const hookPromises = data.hooks.map(hook => 
        this.insertHookResult(connection, evaluationId, hook)
      );
      
      await Promise.all(hookPromises);
      await connection.commit();
      
      return evaluationId;
    } catch (error) {
      await connection.rollback();
      throw error;
    } finally {
      connection.release();
    }
  }
}

Troubleshooting Guide

Common Issues and Solutions

#### 1. MCP Inspector Connection Issues

Problem: Inspector fails to connect to MCP server Diagnosis:

bash

<h1 class="text-3xl font-bold mt-12 mb-8">Check if server starts correctly</h1>
node your-server.js
<h1 class="text-3xl font-bold mt-12 mb-8">Verify Inspector installation</h1>
npx @modelcontextprotocol/inspector --version
<h1 class="text-3xl font-bold mt-12 mb-8">Test manual connection</h1>
npx @modelcontextprotocol/inspector --config ./mcp.json

Solutions:

• Verify MCP server configuration file exists and is valid
• Check transport type matches server implementation
• Increase timeout values for slow-starting servers
• Verify port availability for network transports

#### 2. Hook Execution Failures Problem: Python hooks fail to execute or return errors Diagnosis:

bash

<h1 class="text-3xl font-bold mt-12 mb-8">Test hook individually</h1>
python3 .claude/hooks/functionality-match.py /path/to/project
<h1 class="text-3xl font-bold mt-12 mb-8">Check Python dependencies</h1>
pip3 list 
grep -E "(requests pyyaml
tabulate)"
<h1 class="text-3xl font-bold mt-12 mb-8">Verify hook permissions</h1>
ls -la .claude/hooks/*.py

Solutions:

bash

<h1 class="text-3xl font-bold mt-12 mb-8">Install missing dependencies</h1>
pip3 install requests pyyaml tabulate
<h1 class="text-3xl font-bold mt-12 mb-8">Fix permissions</h1>
chmod +x .claude/hooks/*.py
<h1 class="text-3xl font-bold mt-12 mb-8">Reinstall hooks</h1>
cp -r /path/to/mcp-hooks .claude/hooks

#### 3. Performance Issues

Problem: Evaluations running slowly or timing out Diagnosis:

javascript

// Enable performance monitoring
const evaluator = new MCPEvaluator({
  serverPath: '/path/to/server',
  debug: true,
  timeout: 60000,
  performanceMonitoring: true
});
evaluator.on('performance:metric', (data) => {
  console.log(${data.operation}: ${data.duration}ms);
});

Solutions:

• Increase timeout values in configuration
• Enable caching for repeated evaluations
• Use --static-only for faster development cycles
• Optimize MCP server startup time

#### 4. WebSocket Connection Problems Problem: Dashboard loses connection or events don't update Diagnosis:

javascript

// Client-side debugging
const ws = new WebSocket('ws://localhost:3457/ws');
ws.onopen = () => console.log('Connected');
ws.onerror = (error) => console.error('WebSocket error:', error);
ws.onclose = (event) => console.log('Disconnected:', event.code, event.reason);
// Server-side debugging
io.engine.on("connection_error", (err) => {
  console.log('Connection error:', err.req, err.code, err.message, err.context);
});

Solutions:

• Check firewall settings and port availability
• Verify CORS configuration for cross-origin requests
• Implement connection retry logic with exponential backoff
• Use polling transport as fallback

#### 5. Memory Leaks and Resource Issues Problem: High memory usage or process crashes Diagnosis:

bash

<h1 class="text-3xl font-bold mt-12 mb-8">Monitor memory usage</h1>
node --inspect --max-old-space-size=4096 src/dashboard/server.js
<h1 class="text-3xl font-bold mt-12 mb-8">Use Node.js memory profiling</h1>
node --prof src/evaluator.js
node --prof-process isolate-*.log > profile.txt

Solutions:

javascript

// Implement proper cleanup
class MCPEvaluator {
  async cleanup() {
    // Stop all running processes
    if (this.inspector.process) {
      this.inspector.process.kill('SIGTERM');
    }
    
    // Clear caches
    this.cache.clear();
    
    // Remove event listeners
    this.removeAllListeners();
    
    // Close database connections
    if (this.db) {
      await this.db.close();
    }
  }
}
// Graceful shutdown handling
process.on('SIGINT', async () => {
  console.log('Graceful shutdown initiated');
  await evaluator.cleanup();
  process.exit(0);
});

Debug Mode Configuration

Enable comprehensive debugging with environment variables:

bash

export MCP_DEBUG=true
export MCP_LOG_LEVEL=verbose
export MCP_TRACE_HOOKS=true
export MCP_INSPECTOR_DEBUG=true
<h1 class="text-3xl font-bold mt-12 mb-8">Start with debug logging</h1>
mcp-evaluate /path/to/server --verbose 2>&1 | tee debug.log

Log Analysis Tools

bash

<h1 class="text-3xl font-bold mt-12 mb-8">Extract evaluation timings</h1>
grep "duration:" debug.log awk '{print $3}' sort -n
<h1 class="text-3xl font-bold mt-12 mb-8">Find error patterns</h1>
grep -E "(Error
Failed Timeout)" debug.log sort
 uniq -c
<h1 class="text-3xl font-bold mt-12 mb-8">Monitor WebSocket events</h1>
grep "websocket" debug.log | tail -f
<h1 class="text-3xl font-bold mt-12 mb-8">Hook execution analysis</h1>
grep "hook:" debug.log 
cut -d' ' -f3- sort

This technical documentation provides a comprehensive foundation for understanding, deploying, and maintaining the MCP Testing Evaluation Framework. For API-specific details, security guidelines, and integration examples, refer to the companion documentation files.

Triepod.ai

MCP Testing Framework - Technical Documentation

MCP Testing Evaluation Framework - Technical Documentation

Table of Contents

System Architecture Overview

Core Architecture Principles

Component Specifications

1. MCPEvaluator Core (`src/evaluator.js`)

2. Inspector Bridge (`src/inspector-bridge.js`)

3. CLI Tool (`bin/mcp-evaluate`)

4. Dashboard Server (`src/dashboard/server.js`)

Data Flow Architecture

Evaluation Lifecycle Data Flow

Data Processing Pipeline

Module Interactions

Hook System Integration

Inspector Bridge Communication

Configuration Management

Configuration Hierarchy

Configuration Schema

Dynamic Configuration Loading

Deployment Architecture

Local Development Setup

Production Deployment

Kubernetes Deployment

Performance Optimization

Execution Performance Tuning

Database Optimization

Troubleshooting Guide

Common Issues and Solutions

Debug Mode Configuration

Log Analysis Tools

MCP Testing Framework - Technical Documentation

MCP Testing Evaluation Framework - Technical Documentation

Table of Contents

System Architecture Overview

Core Architecture Principles

Component Specifications

1. MCPEvaluator Core (src/evaluator.js)

2. Inspector Bridge (src/inspector-bridge.js)

3. CLI Tool (bin/mcp-evaluate)

4. Dashboard Server (src/dashboard/server.js)

Data Flow Architecture

Evaluation Lifecycle Data Flow

Data Processing Pipeline

Module Interactions

Hook System Integration

Inspector Bridge Communication

Configuration Management

Configuration Hierarchy

Configuration Schema

Dynamic Configuration Loading

Deployment Architecture

Local Development Setup

Production Deployment

Kubernetes Deployment

Performance Optimization

Execution Performance Tuning

Database Optimization

Troubleshooting Guide

Common Issues and Solutions

Debug Mode Configuration

Log Analysis Tools

1. MCPEvaluator Core (`src/evaluator.js`)

2. Inspector Bridge (`src/inspector-bridge.js`)

3. CLI Tool (`bin/mcp-evaluate`)

4. Dashboard Server (`src/dashboard/server.js`)