Claude MCP Server Ecosystem

# MCP Server Tests This directory contains tests for the MCP Server components, including unit tests, integration tests, and performance tests. ## Test Structure The tests are organized into the following directories: - `attention-mechanisms/`: Tests for the Week 14 Attention Mechanisms components - `language-model/`: Tests for the Week 15 Language Model Interface components - `integration/`: Tests for integration between components - `performance/`: Tests for performance under load - `reports/`: Generated test reports ## Running Tests Before running the tests, you need to start the servers: ```bash # Start the servers needed for testing npm run test:start-servers ``` Then, in a separate terminal, you can run the tests using the following npm scripts: ```bash # Run unit tests for attention mechanisms and language model components npm run test:attention # Run integration tests npm run test:integration # Run performance tests npm run test:performance # Run all tests npm run test:all ``` Alternatively, you can start the servers individually: ```bash # Start Week 14 Attention Mechanisms servers npm run start:week-14 # Start Week 15 Language Model Interface servers npm run start:week-15 ``` ## Test Types ### Unit Tests Unit tests verify the functionality of individual components, including: - Attention Pattern Analyzer - Sparse Attention Engine - Memory-Efficient Attention - Language Model Interface ### Integration Tests Integration tests verify that different components can communicate with each other and that the BaseMCPServer inheritance works correctly across components. ### Performance Tests Performance tests evaluate the components under load, including: - Large attention matrices (1000x1000+) - Memory usage with the Memory-Efficient Attention component - Token counting accuracy in Language Model Interface - Concurrent requests ## Test Reports Test reports are generated in the `reports/` directory. Performance test reports include detailed metrics on memory usage, processing time, and other performance indicators. ## Prerequisites Before running the tests, make sure the required services are running: ```bash # Start Week 14 Attention Mechanisms servers npm run start:week-14 # Start Week 15 Language Model Interface servers npm run start:week-15 # Check the health of the servers npm run health:week-14 npm run health:week-15 ``` ## Test Implementation Details ### Attention Mechanisms Tests - `attention-pattern-analyzer.test.js`: Tests for the Attention Pattern Analyzer component - `sparse-attention-engine.test.js`: Tests for the Sparse Attention Engine component - `memory-efficient-attention.test.js`: Tests for the Memory-Efficient Attention component ### Language Model Interface Tests - `language-model-interface.test.js`: Tests for the Language Model Interface component ### Integration Tests - `component-integration.test.js`: Tests for integration between components ### Performance Tests - `performance-tests.js`: Tests for performance under load ## Test Configuration The tests are configured to run against the following server ports: - Attention Pattern Analyzer: `http://localhost:8000` - Sparse Attention Engine: `http://localhost:8001` - Memory-Efficient Attention: `http://localhost:8002` - Language Model Interface: `http://localhost:8003` - Attention Visualization Engine: `http://localhost:8004` - Cross-Attention Controller: `http://localhost:8005` - Inference Pipeline Manager: `http://localhost:8006` - Model Benchmarking Suite: `http://localhost:8007` - Model Integration Hub: `http://localhost:8008` ## Test Utilities The tests use the following utilities: - `run-tests.js`: Script to run the tests - HTTP request helpers for making requests to the servers - Sample data generators for creating test data ## Test Coverage The tests cover the following areas: 1. **Integration Testing** - Component communication - BaseMCPServer inheritance - Cross-component data flow 2. **Real Data Validation** - Attention matrices - Transformer model outputs - Pattern analysis 3. **Performance Under Load** - Large attention matrices - Memory usage - Token counting accuracy - Concurrent requests