Test Planning & Strategy

Test Planning & Strategy

Effective MCP testing requires careful planning and a systematic approach. This guide provides frameworks, strategies, and best practices for comprehensive MCP implementation testing using MCP Client Tester.

Testing Framework Overview

MCP testing should cover multiple dimensions to ensure comprehensive validation:

graph TD
    A[MCP Testing Strategy] --> B[Protocol Compliance]
    A --> C[Client Compatibility]
    A --> D[Performance Validation]
    A --> E[Error Handling]
    A --> F[Security Testing]

    B --> B1[Message Format]
    B --> B2[Method Signatures]
    B --> B3[Transport Behavior]

    C --> C1[Client Detection]
    C --> C2[Feature Support]
    C --> C3[Version Compatibility]

    D --> D1[Response Times]
    D --> D2[Throughput]
    D --> D3[Resource Usage]

    E --> E1[Invalid Requests]
    E --> E2[Network Failures]
    E --> E3[Recovery Scenarios]

    F --> F1[Authentication]
    F --> F2[Authorization]
    F --> F3[Input Validation]

Test Categories

1. Protocol Compliance Testing

Ensure your MCP implementation correctly follows the protocol specification.

Core Protocol Elements:

• JSON-RPC 2.0 message format compliance
• Required method implementations
• Parameter validation and error responses
• Transport-specific behavior

Test Cases:

1. Initialization Sequence

   • Verify proper handshake completion
   • Check protocol version negotiation
   • Validate capability exchange

2. Message Format Validation

   • Test JSON-RPC 2.0 compliance
   • Verify request/response/notification formats
   • Check error response structure

3. Method Implementation

   • Test all implemented methods
   • Verify parameter handling
   • Check response format compliance

4. Transport Behavior

   • Test connection establishment
   • Verify message delivery
   • Check connection cleanup

2. Feature Testing

Validate specific MCP features work correctly across different scenarios.

Tools Testing:

{
  "test_suite": "tools",
  "test_cases": [
    {
      "name": "tool_discovery",
      "method": "tools/list",
      "expected_fields": ["name", "description", "inputSchema"]
    },
    {
      "name": "tool_execution",
      "method": "tools/call",
      "test_cases": [
        {"args": {"valid": "input"}, "expect": "success"},
        {"args": {"invalid": true}, "expect": "error"},
        {"args": {}, "expect": "validation_error"}
      ]
    }
  ]
}

Resources Testing:

• Resource discovery and listing
• Content reading with various formats
• URI handling and validation
• Subscription to resource changes

Prompts Testing:

• Template discovery
• Parameter substitution
• Dynamic prompt generation
• Error handling for invalid templates

3. Client Compatibility Testing

Ensure your MCP server works correctly with different client implementations.

Multi-Client Test Matrix:

Test Scenario	Claude Desktop	FastMCP	Python SDK	Custom Client
Basic Connection	✓	✓	✓	?
Tool Calling	✓	✓	✓	?
Resource Access	✓	✓	✓	?
Error Handling	✓	✓	✓	?
Progress Updates	✓	✓	✓	?

Client-Specific Tests:

# Example test configuration for multiple clients
CLIENT_CONFIGS = {
    "claude-desktop": {
        "transport": "stdio",
        "features": ["tools", "resources", "prompts", "progress"],
        "limitations": ["no_sampling", "no_http_transport"]
    },
    "fastmcp-client": {
        "transport": "http",
        "features": ["tools", "resources", "prompts", "sampling", "progress"],
        "limitations": []
    },
    "custom-client": {
        "transport": "http",
        "features": ["tools", "resources"],
        "limitations": ["no_prompts", "basic_error_handling"]
    }
}

4. Performance Testing

Validate system performance under various load conditions.

Performance Metrics:

• Response time (average, p95, p99)
• Throughput (messages per second)
• Memory usage
• Connection overhead
• Error rates

Load Testing Scenarios:

Baseline Performance

• Single client, sequential requests
• Measure baseline response times
• Establish performance benchmarks

Concurrent Connections

• Multiple simultaneous clients
• Measure resource contention
• Test connection limits

High Throughput

• Rapid request sequences
• Large payload handling
• Queue saturation testing

Stress Testing

• Beyond normal operating limits
• Resource exhaustion scenarios
• Recovery behavior validation

5. Error Handling & Recovery

Test system behavior under various failure conditions.

Error Scenarios:

• Network interruptions
• Invalid message formats
• Unknown method calls
• Parameter validation failures
• Resource unavailability
• Timeout conditions

Recovery Testing:

• Connection re-establishment
• State synchronization
• Graceful degradation
• Error propagation

Test Environment Setup

Development Environment

Local Testing Setup:

docker-compose.test.yml

services:
  mcp-client-tester:
    build: .
    environment:
      - ENVIRONMENT=testing
      - LOG_LEVEL=DEBUG
      - ENABLE_TEST_TOOLS=true
    volumes:
      - ./test-data:/app/test-data

  test-clients:
    build: ./test-clients
    depends_on:
      - mcp-client-tester
    environment:
      - MCP_SERVER_URL=http://mcp-client-tester:8000

Test Data Management:

test_data_setup.py

def setup_test_environment():
    """Initialize test environment with sample data"""

    # Create test sessions
    test_sessions = [
        {"name": "Protocol Compliance", "transport": "stdio"},
        {"name": "Performance Baseline", "transport": "http"},
        {"name": "Error Handling", "transport": "sse"}
    ]

    # Setup test tools and resources
    setup_test_tools()
    setup_test_resources()

    # Configure client simulators
    setup_client_simulators()

Staging Environment

Production-like Testing:

• Multiple server instances
• Load balancer configuration
• Database replication
• Monitoring and logging
• Security controls

Continuous Integration

CI/CD Pipeline Integration:

.github/workflows/mcp-testing.yml

name: MCP Testing Pipeline

on: [push, pull_request]

jobs:
  protocol-tests:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3

      - name: Start MCP Client Tester
        run: docker-compose up -d

      - name: Run Protocol Compliance Tests
        run: |
          python -m pytest tests/protocol/ \
            --mcp-server=http://localhost:8000 \
            --junit-xml=protocol-results.xml

      - name: Run Client Compatibility Tests
        run: |
          python -m pytest tests/clients/ \
            --junit-xml=client-results.xml

      - name: Generate Test Report
        run: |
          python scripts/generate_test_report.py \
            --results protocol-results.xml client-results.xml

Test Scenarios & Use Cases

Basic Functionality Tests

Essential Test Scenarios:

1. Connection Lifecycle

   async def test_connection_lifecycle():
       # Test connection establishment
       session = await create_test_session("stdio")
       assert session.status == "active"
   
       # Test normal operation
       tools = await session.list_tools()
       assert len(tools) > 0
   
       # Test graceful shutdown
       await session.close()
       assert session.status == "closed"

2. Tool Interaction Flow

   async def test_tool_interaction():
       session = await create_test_session("http")
   
       # Discover tools
       tools = await session.list_tools()
       search_tool = find_tool(tools, "search_database")
   
       # Call tool with valid parameters
       result = await session.call_tool(
           "search_database",
           {"query": "test", "limit": 5}
       )
       assert result.success
   
       # Call tool with invalid parameters
       with pytest.raises(ValidationError):
           await session.call_tool(
               "search_database",
               {"invalid": "params"}
           )

Advanced Scenarios

Complex Integration Tests:

1. Multi-step Workflows

   async def test_multi_step_workflow():
       session = await create_test_session("http")
   
       # Step 1: Search for data
       search_results = await session.call_tool(
           "search_database", {"query": "user:123"}
       )
   
       # Step 2: Process results
       user_data = search_results.content[0].data
       processed = await session.call_tool(
           "process_data", {"data": user_data}
       )
   
       # Step 3: Generate report
       report = await session.call_tool(
           "generate_report", {"data": processed.content}
       )
   
       assert report.success

2. Progress Tracking

   async def test_long_running_operation():
       session = await create_test_session("sse")
   
       # Start long operation
       operation_id = await session.call_tool(
           "process_large_dataset",
           {"dataset": "large_data.csv"}
       )
   
       # Monitor progress
       progress_updates = []
       async for update in session.listen_progress(operation_id):
           progress_updates.append(update)
           if update.complete:
               break
   
       assert len(progress_updates) > 0
       assert progress_updates[-1].complete

Error Scenario Testing

Failure Mode Testing:

async def test_error_scenarios():
    session = await create_test_session("http")

    # Test invalid method
    with pytest.raises(MethodNotFoundError):
        await session.call_method("invalid_method")

    # Test malformed parameters
    with pytest.raises(InvalidParamsError):
        await session.call_tool("valid_tool", "invalid_params")

    # Test resource not found
    with pytest.raises(ResourceNotFoundError):
        await session.read_resource("nonexistent://resource")

    # Test timeout scenario
    with pytest.raises(TimeoutError):
        await session.call_tool(
            "slow_tool", timeout=1.0
        )

Test Automation Strategies

Automated Test Suites

Comprehensive Test Coverage:

class MCPTestSuite:
    """Automated test suite for MCP implementations"""

    def __init__(self, server_config):
        self.server_config = server_config
        self.results = TestResults()

    async def run_all_tests(self):
        """Execute complete test suite"""

        # Protocol compliance tests
        await self.run_protocol_tests()

        # Feature functionality tests
        await self.run_feature_tests()

        # Client compatibility tests
        await self.run_compatibility_tests()

        # Performance tests
        await self.run_performance_tests()

        # Error handling tests
        await self.run_error_tests()

        return self.results

    async def run_protocol_tests(self):
        """Test protocol compliance"""
        for transport in ["stdio", "http", "sse"]:
            session = await self.create_session(transport)

            # Test initialization
            await self.test_initialization(session)

            # Test message formats
            await self.test_message_formats(session)

            # Test method signatures
            await self.test_method_signatures(session)

Regression Testing

Automated Regression Detection:

class RegressionTestSuite:
    """Detect regressions in MCP implementations"""

    def __init__(self, baseline_results):
        self.baseline = baseline_results
        self.current_results = None

    def compare_results(self, current_results):
        """Compare current results against baseline"""

        regressions = []

        # Performance regression detection
        if current_results.avg_response_time > self.baseline.avg_response_time * 1.5:
            regressions.append("Performance regression detected")

        # Feature regression detection
        baseline_features = set(self.baseline.supported_features)
        current_features = set(current_results.supported_features)

        missing_features = baseline_features - current_features
        if missing_features:
            regressions.append(f"Missing features: {missing_features}")

        return regressions

Test Data Management

Test Data Strategy

Structured Test Data:

# Test data organization
TEST_DATA = {
    "tools": {
        "valid_calls": [
            {"name": "search", "args": {"query": "test"}},
            {"name": "calculate", "args": {"expression": "2+2"}},
        ],
        "invalid_calls": [
            {"name": "nonexistent", "args": {}},
            {"name": "search", "args": {"invalid": "param"}},
        ]
    },
    "resources": {
        "available": [
            "file:///test/data.txt",
            "http://example.com/api/data",
        ],
        "unavailable": [
            "file:///nonexistent.txt",
            "http://invalid.domain/data",
        ]
    }
}

Data Generation

Dynamic Test Data:

def generate_test_scenarios(complexity="medium"):
    """Generate test scenarios based on complexity level"""

    scenarios = []

    if complexity == "basic":
        scenarios = generate_basic_scenarios()
    elif complexity == "medium":
        scenarios = generate_medium_scenarios()
    elif complexity == "advanced":
        scenarios = generate_advanced_scenarios()

    return scenarios

def generate_performance_test_data(size_category):
    """Generate test data for performance testing"""

    data_sizes = {
        "small": {"records": 100, "size_kb": 10},
        "medium": {"records": 10000, "size_kb": 1000},
        "large": {"records": 1000000, "size_kb": 100000}
    }

    return create_test_dataset(data_sizes[size_category])

Monitoring & Reporting

Test Metrics

Key Performance Indicators:

• Test Coverage: Percentage of protocol features tested
• Success Rate: Ratio of passing tests to total tests
• Performance Benchmarks: Response time percentiles
• Client Compatibility: Support matrix completion
• Error Handling: Recovery success rates

Reporting Framework

Automated Report Generation:

class TestReportGenerator:
    """Generate comprehensive test reports"""

    def generate_executive_summary(self, results):
        """High-level summary for stakeholders"""
        return {
            "overall_score": calculate_overall_score(results),
            "critical_issues": identify_critical_issues(results),
            "recommendations": generate_recommendations(results)
        }

    def generate_technical_report(self, results):
        """Detailed technical analysis"""
        return {
            "protocol_compliance": results.protocol_score,
            "performance_analysis": results.performance_metrics,
            "client_compatibility": results.compatibility_matrix,
            "error_analysis": results.error_patterns
        }

Tip

Use MCP Client Tester’s built-in reporting features to generate professional test reports automatically. Export data in various formats for integration with existing reporting systems.

Best Practices

Test Design Principles

1. Comprehensive Coverage: Test all protocol features and edge cases
2. Realistic Scenarios: Use production-like test conditions
3. Automated Execution: Minimize manual testing overhead
4. Continuous Validation: Integrate with development workflows
5. Clear Reporting: Provide actionable insights

Common Pitfalls to Avoid

1. Insufficient Error Testing: Don’t only test happy paths
2. Single Client Focus: Test with multiple client implementations
3. Performance Assumptions: Always measure actual performance
4. Static Test Data: Use varied and realistic test scenarios
5. Manual Processes: Automate repetitive testing tasks

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Ready to implement your testing strategy? Continue with Protocol Validation for specific testing techniques or explore Performance Testing for load testing strategies.