my-pal-mcp-server/docs/testing.md

Testing Guide

This project includes comprehensive test coverage through unit tests and integration simulator tests.

Running Tests

Prerequisites

• Environment set up: ./run-server.sh
  • Use ./run-server.sh -f to automatically follow logs after starting

Unit Tests

Run all unit tests with pytest:

# Run all tests with verbose output
python -m pytest -xvs

# Run specific test file
python -m pytest tests/test_providers.py -xvs

Simulator Tests

Simulator tests replicate real-world Claude CLI interactions with the standalone MCP server. Unlike unit tests that test isolated functions, simulator tests validate the complete end-to-end flow including:

• Actual MCP protocol communication
• Standalone server interactions
• Multi-turn conversations across tools
• Log output validation

Important: Simulator tests require LOG_LEVEL=DEBUG in your .env file to validate detailed execution logs.

Monitoring Logs During Tests

Important: The MCP stdio protocol interferes with stderr output during tool execution. Tool execution logs are written to local log files. This is a known limitation of the stdio-based MCP protocol.

To monitor logs during test execution:

# Start server and automatically follow logs
./run-server.sh -f

# Or manually monitor main server logs (includes all tool execution details)
tail -f -n 500 logs/mcp_server.log

# Monitor MCP activity logs (tool calls and completions)  
tail -f logs/mcp_activity.log

# Check log file sizes (logs rotate at 20MB)
ls -lh logs/mcp_*.log*

Log Rotation: All log files are configured with automatic rotation at 20MB to prevent disk space issues. The server keeps:

• 10 rotated files for mcp_server.log (200MB total)
• 5 rotated files for mcp_activity.log (100MB total)

Why logs appear in files: The MCP stdio_server captures stderr during tool execution to prevent interference with the JSON-RPC protocol communication. This means tool execution logs are written to files rather than displayed in console output.

Running All Simulator Tests

# Run all simulator tests
python communication_simulator_test.py

# Run with verbose output for debugging
python communication_simulator_test.py --verbose

# Keep server logs after tests for inspection
python communication_simulator_test.py --keep-logs

Running Individual Tests

To run a single simulator test in isolation (useful for debugging or test development):

# Run a specific test by name
python communication_simulator_test.py --individual basic_conversation

# Examples of available tests:
python communication_simulator_test.py --individual content_validation
python communication_simulator_test.py --individual cross_tool_continuation
python communication_simulator_test.py --individual memory_validation

Other Options

# List all available simulator tests with descriptions
python communication_simulator_test.py --list-tests

# Run multiple specific tests (not all)
python communication_simulator_test.py --tests basic_conversation content_validation

Code Quality Checks

Before committing, ensure all linting passes:

# Run all linting checks
ruff check .
black --check .
isort --check-only .

# Auto-fix issues
ruff check . --fix
black .
isort .

What Each Test Suite Covers

Unit Tests

Test isolated components and functions:

• Provider functionality: Model initialization, API interactions, capability checks
• Tool operations: All MCP tools (chat, analyze, debug, etc.)
• Conversation memory: Threading, continuation, history management
• File handling: Path validation, token limits, deduplication
• Auto mode: Model selection logic and fallback behavior

Simulator Tests

Validate real-world usage scenarios by simulating actual Claude prompts:

• Basic conversations: Multi-turn chat functionality with real prompts
• Cross-tool continuation: Context preservation across different tools
• File deduplication: Efficient handling of repeated file references
• Model selection: Proper routing to configured providers
• Token allocation: Context window management in practice
• Redis validation: Conversation persistence and retrieval

Contributing

For detailed contribution guidelines, testing requirements, and code quality standards, please see our Contributing Guide.

Quick Testing Reference

# Run quality checks
./code_quality_checks.sh

# Run unit tests
python -m pytest -xvs

# Run simulator tests (for tool changes)
python communication_simulator_test.py

Remember: All tests must pass before submitting a PR. See the Contributing Guide for complete requirements.