proffesor-for-testing__agentic-qe/docs/MCP-TOOLS-USER-GUIDE.md

AQE MCP Tools User Guide v1.3.5

Last Updated: 2025-10-30 Verification Status: Production-Ready (70% verified working)

Quick Reference

✅ Production-Ready Tools (14 tools)

These tools are fully tested and safe for production use:

Memory & Coordination

• memory_store - Store data with TTL and namespacing
• memory_retrieve - Retrieve stored data
• memory_query - Query with pattern matching
• memory_share - Share data between agents
• memory_backup - Backup/restore memory
• blackboard_post - Post coordination hints
• blackboard_read - Read coordination hints
• consensus_propose - Create consensus proposals
• consensus_vote - Vote on proposals

Quality & Analysis

• quality_analyze 🔧 - Analyze quality metrics (RECENTLY FIXED)
• regression_risk_analyze 🔧 - Analyze regression risk (RECENTLY FIXED)
• quality_validate_metrics - Validate against thresholds
• coverage_analyze_sublinear - O(log n) coverage analysis
• coverage_gaps_detect - Detect and prioritize gaps

Fleet Management

• fleet_init - Initialize QE fleet
• fleet_status - Get fleet status

Enhanced Testing

• test_generate_enhanced - AI-powered test generation
• test_execute_parallel - Parallel test execution
• test_optimize_sublinear - Sublinear test optimization

⚠️ Use with Caution (2 tools)

These tools work but may show non-fatal warnings:

• agent_spawn - Spawns agents (database warnings, functional)
• test_execute - Executes tests (database warnings, functional)

🚧 Not Recommended (1 tool)

These tools need fixes before production use:

• test_generate - Task validation error (use test_generate_enhanced instead)

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Detailed Tool Documentation

Memory Management Tools

memory_store

Status: ✅ Production-Ready Description: Store data with TTL support and namespacing

Usage:

mcp__agentic_qe__memory_store({
  key: 'test/results/run-123',
  value: { passed: 45, failed: 2, duration: 1234 },
  namespace: 'test-results',
  ttl: 86400, // 24 hours
  persist: true // Optional: persist to database
})

Parameters:

• key (required): Memory key (supports path-like structure)
• value (required): Any JSON-serializable data
• namespace (optional): Namespace for isolation (default: 'default')
• ttl (optional): Time to live in seconds (0 for persistent)
• metadata (optional): Additional metadata
• persist (optional): Persist to database (default: false)

memory_retrieve

Status: ✅ Production-Ready Description: Retrieve stored data with optional metadata

Usage:

mcp__agentic_qe__memory_retrieve({
  key: 'test/results/run-123',
  namespace: 'test-results',
  includeMetadata: true // Optional: include metadata
})

Returns: Stored value + optional metadata

memory_query

Status: ✅ Production-Ready Description: Query memory with pattern matching

Usage:

mcp__agentic_qe__memory_query({
  namespace: 'test-results',
  pattern: 'test/results/*', // Supports wildcards
  limit: 50,
  startTime: Date.now() - 86400000, // Last 24 hours
  includeExpired: false
})

Returns: Array of matching entries with keys and values

Coordination Tools

blackboard_post

Status: ✅ Production-Ready Description: Post coordination hints for agent communication

Usage:

mcp__agentic_qe__blackboard_post({
  topic: 'test-execution',
  message: 'Test run completed successfully',
  priority: 'high', // low, medium, high, critical
  agentId: 'test-executor-1',
  metadata: { runId: '123', duration: 1234 },
  ttl: 3600 // 1 hour
})

Use Cases:

• Agent-to-agent communication
• Coordination hints
• Status updates
• Event notifications

blackboard_read

Status: ✅ Production-Ready Description: Read coordination hints from blackboard

Usage:

mcp__agentic_qe__blackboard_read({
  topic: 'test-execution',
  agentId: 'test-coordinator',
  minPriority: 'medium', // Filter by priority
  since: Date.now() - 3600000, // Last hour
  limit: 20
})

Returns: Array of hints with timestamps and metadata

Quality Analysis Tools

quality_analyze 🔧 (RECENTLY FIXED)

Status: ✅ Production-Ready Description: Analyze quality metrics with recommendations

What Was Fixed:

• context parameter in dataSource is now optional
• Works with minimal parameters
• Defaults applied automatically

Usage:

mcp__agentic_qe__quality_analyze({
  params: {
    scope: 'code', // code, tests, performance, security, all
    metrics: ['complexity', 'maintainability'],
    generateRecommendations: true,
    historicalComparison: false
  },
  dataSource: {
    codeMetrics: {
      files: [
        { name: 'src/module.ts', loc: 500, cyclomatic: 12 }
      ]
    }
    // context is OPTIONAL now!
  }
})

Returns:

{
  "scope": "code",
  "metrics": {
    "complexity": {
      "average": 12,
      "max": 25,
      "files": [...]
    }
  },
  "recommendations": [
    "Refactor functions with complexity >15",
    "Break down large modules"
  ],
  "qualityScore": 7.5
}

regression_risk_analyze 🔧 (RECENTLY FIXED)

Status: ✅ Production-Ready Description: Analyze regression risk for code changes

What Was Fixed:

• Simplified parameter format
• baselineMetrics optional (uses defaults)
• Works with minimal change data

Usage:

mcp__agentic_qe__regression_risk_analyze({
  changes: [
    {
      file: 'src/auth/login.ts',
      type: 'modify', // modify, add, delete, refactor
      linesChanged: 50
    },
    {
      file: 'src/auth/register.ts',
      type: 'refactor',
      linesChanged: 120
    }
  ],
  threshold: 0.2 // Optional: risk threshold (default: 0.1)
})

Returns:

{
  "overallRisk": 0.45,
  "riskLevel": "medium",
  "fileRisks": [
    {
      "file": "src/auth/login.ts",
      "risk": 0.35,
      "factors": ["high change frequency", "critical path"]
    }
  ],
  "recommendations": [
    "Add regression tests for src/auth/login.ts",
    "Run full integration test suite"
  ]
}

Coverage Analysis Tools

coverage_analyze_sublinear

Status: ✅ Production-Ready Description: Ultra-fast O(log n) coverage analysis

Usage:

mcp__agentic_qe__coverage_analyze_sublinear({
  sourceFiles: ['src/**/*.ts'],
  coverageThreshold: 0.8, // 80%
  useJohnsonLindenstrauss: true, // Enable O(log n) optimization
  includeUncoveredLines: true
})

Performance:

• Traditional: O(n) - 1000 files = 1000 operations
• Sublinear: O(log n) - 1000 files = 10 operations
• ~100x faster for large codebases

Returns:

{
  "totalFiles": 1000,
  "coveragePercent": 82.5,
  "dimensionReduction": {
    "original": 1000,
    "reduced": 10,
    "algorithm": "johnson-lindenstrauss"
  },
  "gaps": [...],
  "uncoveredLines": [...]
}

coverage_gaps_detect

Status: ✅ Production-Ready Description: Detect and prioritize coverage gaps

Usage:

mcp__agentic_qe__coverage_gaps_detect({
  coverageData: {
    files: [
      {
        path: 'src/critical.ts',
        coverage: { statements: 60, branches: 45, functions: 70 }
      }
    ]
  },
  prioritization: 'complexity' // complexity, criticality, change-frequency
})

Returns:

{
  "gaps": [
    {
      "file": "src/critical.ts",
      "priority": "high",
      "uncoveredStatements": 40,
      "uncoveredBranches": 55,
      "recommendations": [
        "Add tests for error handling paths",
        "Cover edge cases in validation logic"
      ]
    }
  ],
  "totalGaps": 15,
  "priorityDistribution": {
    "high": 3,
    "medium": 7,
    "low": 5
  }
}

Fleet Management Tools

fleet_init

Status: ✅ Production-Ready Description: Initialize QE fleet with topology

Usage:

mcp__agentic_qe__fleet_init({
  config: {
    topology: 'hierarchical', // hierarchical, mesh, ring, adaptive
    maxAgents: 10,
    testingFocus: ['unit', 'integration'],
    environments: ['development', 'staging'],
    frameworks: ['jest', 'mocha']
  },
  projectContext: { // Optional
    repositoryUrl: 'https://github.com/org/repo',
    language: 'typescript',
    buildSystem: 'npm'
  }
})

Returns:

{
  "fleetId": "fleet-1761822142157-d39f85af71",
  "topology": "hierarchical",
  "maxAgents": 10,
  "status": "initialized",
  "coordinationChannels": 5
}

agent_spawn ⚠️

Status: ⚠️ Works with warnings Description: Spawn specialized QE agent

Known Issues: May show database initialization warnings (non-fatal)

Usage:

mcp__agentic_qe__agent_spawn({
  spec: {
    type: 'test-generator', // test-generator, coverage-analyzer, quality-gate, etc.
    capabilities: ['unit-testing', 'integration-testing'],
    resources: { // Optional
      memory: 512,
      cpu: 1,
      storage: 1024
    }
  },
  fleetId: 'fleet-1761822142157-d39f85af71' // Optional
})

Expected Warnings (can be ignored):

Database connection failed - not initialized
Failed to load from database: Database not initialized

Workaround: Agent continues to function despite warnings. These warnings indicate the agent is using in-memory fallback for knowledge storage.

Enhanced Testing Tools

test_generate_enhanced

Status: ✅ Production-Ready Description: AI-powered test generation with pattern recognition

Usage:

mcp__agentic_qe__test_generate_enhanced({
  sourceCode: 'function add(a, b) { return a + b; }',
  language: 'javascript',
  testType: 'unit', // unit, integration, e2e, property-based
  aiEnhancement: true,
  coverageGoal: 90,
  detectAntiPatterns: true
})

Returns:

{
  "tests": "describe('add', () => { it('should add numbers', () => { ... }) });",
  "coverageEstimate": 95,
  "patterns": ["arithmetic-operation", "pure-function"],
  "antiPatterns": [],
  "suggestions": [
    "Consider edge cases: negative numbers, zero, floats"
  ]
}

test_execute_parallel

Status: ✅ Production-Ready Description: Execute tests with worker pools

Usage:

mcp__agentic_qe__test_execute_parallel({
  testFiles: ['tests/unit/*.test.js'],
  parallelism: 4, // Number of workers
  timeout: 5000,
  retryFailures: true,
  maxRetries: 3,
  loadBalancing: 'round-robin' // round-robin, least-loaded, random
})

Performance:

• Sequential: 100 tests × 50ms = 5000ms
• Parallel (4 workers): 100 tests × 50ms ÷ 4 = 1250ms
• 4x faster execution

Common Use Cases

Use Case 1: Continuous Quality Monitoring

Goal: Monitor quality metrics across builds

// 1. Store test results
mcp__agentic_qe__memory_store({
  key: `build/${buildId}/results`,
  value: testResults,
  namespace: 'ci-cd',
  ttl: 2592000 // 30 days
});

// 2. Analyze quality
const analysis = mcp__agentic_qe__quality_analyze({
  params: {
    scope: 'all',
    metrics: ['coverage', 'complexity', 'test-pass-rate'],
    historicalComparison: true
  },
  dataSource: {
    testResults: `build/${buildId}/results`,
    codeMetrics: codeMetricsData
  }
});

// 3. Post to blackboard for coordination
mcp__agentic_qe__blackboard_post({
  topic: 'quality-alerts',
  message: `Build ${buildId} quality: ${analysis.qualityScore}/10`,
  priority: analysis.qualityScore < 7 ? 'high' : 'medium',
  agentId: 'quality-monitor'
});

Use Case 2: Regression Risk Assessment

Goal: Assess risk before merging PR

// 1. Analyze regression risk
const riskAnalysis = mcp__agentic_qe__regression_risk_analyze({
  changes: pullRequestChanges,
  threshold: 0.3
});

// 2. Detect coverage gaps
const coverageGaps = mcp__agentic_qe__coverage_gaps_detect({
  coverageData: currentCoverage,
  prioritization: 'criticality'
});

// 3. Make consensus decision
const proposalId = `pr-${prNumber}-merge-decision`;
mcp__agentic_qe__consensus_propose({
  proposalId,
  topic: 'pr-merge',
  proposal: {
    prNumber,
    riskLevel: riskAnalysis.riskLevel,
    coverageGaps: coverageGaps.gaps.length,
    recommendation: riskAnalysis.overallRisk < 0.3 ? 'approve' : 'review'
  },
  votingAgents: ['reviewer-1', 'reviewer-2', 'qa-lead'],
  quorum: 0.67 // 67% approval needed
});

Use Case 3: High-Speed Test Optimization

Goal: Optimize large test suite execution

// 1. Analyze coverage with O(log n) algorithm
const coverage = mcp__agentic_qe__coverage_analyze_sublinear({
  sourceFiles: ['src/**/*.ts'],
  coverageThreshold: 0.85,
  useJohnsonLindenstrauss: true
});

// 2. Optimize test suite
const optimized = mcp__agentic_qe__test_optimize_sublinear({
  testSuite: { tests: allTests },
  algorithm: 'sublinear',
  targetReduction: 0.3, // Reduce to 30% of tests
  maintainCoverage: 0.85
});

// 3. Execute optimized suite in parallel
mcp__agentic_qe__test_execute_parallel({
  testFiles: optimized.selectedTests,
  parallelism: 8,
  collectCoverage: true
});

Troubleshooting

Issue: Database Warnings in agent_spawn

Symptoms:

Database connection failed - not initialized
Failed to load from database: Database not initialized

Severity: Low (non-fatal) Impact: Agent uses in-memory fallback Action: No action needed, agent continues normally

Issue: test_generate Task Validation Error

Symptoms:

Task failed: Invalid task assignment

Severity: High (blocks test generation) Workaround: Use test_generate_enhanced instead Fix Status: In progress (P0)

Issue: Claude Flow Hooks Failing

Symptoms:

Hook command failed: SqliteError: no such column: namespace
External hook failed, using AQE hooks fallback

Severity: Low (transparent fallback) Impact: None (AQE native hooks faster anyway) Action: No action needed

Performance Tips

1. Use Sublinear Algorithms:

   • For codebases >500 files, use coverage_analyze_sublinear
   • 100-150x faster than traditional coverage analysis

2. Enable Parallel Execution:

   • Use test_execute_parallel for large test suites
   • Set parallelism to CPU cores for optimal performance

3. Set Appropriate TTLs:

   • Short-lived data: 3600s (1 hour)
   • Build results: 2592000s (30 days)
   • Permanent: 0 (no expiry)

4. Use Memory Namespaces:

   • Isolate different data types
   • Faster queries with smaller namespace
   • Examples: 'test-results', 'quality-metrics', 'coordination'

Migration Notes

From v1.3.4 to v1.3.5

Breaking Changes: None

New Features:

• quality_analyze: Context optional
• regression_risk_analyze: Simplified parameters
• Improved error messages
• Better fallback handling

Deprecated: None

Support & Feedback

• Documentation: /workspaces/agentic-qe-cf/docs/
• Issues: GitHub Issues
• Production Readiness Report: docs/MCP-PRODUCTION-READINESS-REPORT.md

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Last Verification: 2025-10-30 Next Review: After P0 fixes Status: 70% tools production-ready, 2 tools with warnings, 1 tool needs fix