Migration from Docker to Standalone Python Server (#73)
* Migration from docker to standalone server Migration handling Fixed tests Use simpler in-memory storage Support for concurrent logging to disk Simplified direct connections to localhost * Migration from docker / redis to standalone script Updated tests Updated run script Fixed requirements Use dotenv Ask if user would like to install MCP in Claude Desktop once Updated docs * More cleanup and references to docker removed * Cleanup * Comments * Fixed tests * Fix GitHub Actions workflow for standalone Python architecture - Install requirements-dev.txt for pytest and testing dependencies - Remove Docker setup from simulation tests (now standalone) - Simplify linting job to use requirements-dev.txt - Update simulation tests to run directly without Docker Fixes unit test failures in CI due to missing pytest dependency. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com> * Remove simulation tests from GitHub Actions - Removed simulation-tests job that makes real API calls - Keep only unit tests (mocked, no API costs) and linting - Simulation tests should be run manually with real API keys - Reduces CI costs and complexity GitHub Actions now only runs: - Unit tests (569 tests, all mocked) - Code quality checks (ruff, black) 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com> * Fixed tests * Fixed tests --------- Co-authored-by: Claude <noreply@anthropic.com>
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216
simulator_tests/conversation_base_test.py
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simulator_tests/conversation_base_test.py
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#!/usr/bin/env python3
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"""
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Conversation Base Test Class for In-Process MCP Tool Testing
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This class enables testing MCP tools within the same process to maintain conversation
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memory state across tool calls. Unlike BaseSimulatorTest which runs each tool call
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as a separate subprocess (losing memory state), this class calls tools directly
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in-process, allowing conversation functionality to work correctly.
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USAGE:
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- Inherit from ConversationBaseTest instead of BaseSimulatorTest for conversation tests
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- Use call_mcp_tool_direct() to call tools in-process
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- Conversation memory persists across tool calls within the same test
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- setUp() clears memory between test methods for proper isolation
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EXAMPLE:
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class TestConversationFeature(ConversationBaseTest):
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def test_cross_tool_continuation(self):
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# Step 1: Call precommit tool
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result1, continuation_id = self.call_mcp_tool_direct("precommit", {
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"path": "/path/to/repo",
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"prompt": "Review these changes"
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})
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# Step 2: Continue with codereview tool - memory is preserved!
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result2, _ = self.call_mcp_tool_direct("codereview", {
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"files": ["/path/to/file.py"],
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"prompt": "Focus on security issues",
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"continuation_id": continuation_id
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})
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"""
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import asyncio
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import json
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from typing import Optional
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from .base_test import BaseSimulatorTest
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class ConversationBaseTest(BaseSimulatorTest):
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"""Base class for conversation tests that require in-process tool calling"""
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def __init__(self, verbose: bool = False):
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super().__init__(verbose)
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self._tools = None
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self._loop = None
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def setUp(self):
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"""Set up test environment - clears conversation memory between tests"""
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super().setup_test_files()
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# Clear conversation memory for test isolation
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self._clear_conversation_memory()
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# Import tools from server.py for in-process calling
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if self._tools is None:
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self._import_tools()
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def _clear_conversation_memory(self):
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"""Clear all conversation memory to ensure test isolation"""
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try:
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from utils.storage_backend import get_storage_backend
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storage = get_storage_backend()
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# Clear all stored conversation threads
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with storage._lock:
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storage._store.clear()
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self.logger.debug("Cleared conversation memory for test isolation")
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except Exception as e:
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self.logger.warning(f"Could not clear conversation memory: {e}")
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def _import_tools(self):
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"""Import tools from server.py for direct calling"""
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try:
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import os
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import sys
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# Add project root to Python path if not already there
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project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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if project_root not in sys.path:
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sys.path.insert(0, project_root)
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# Import tools from server
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from server import TOOLS
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self._tools = TOOLS
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self.logger.debug(f"Imported {len(self._tools)} tools for in-process testing")
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except ImportError as e:
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raise RuntimeError(f"Could not import tools from server.py: {e}")
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def _get_event_loop(self):
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"""Get or create event loop for async tool execution"""
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if self._loop is None:
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try:
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self._loop = asyncio.get_event_loop()
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except RuntimeError:
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self._loop = asyncio.new_event_loop()
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asyncio.set_event_loop(self._loop)
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return self._loop
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def call_mcp_tool_direct(self, tool_name: str, params: dict) -> tuple[Optional[str], Optional[str]]:
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"""
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Call an MCP tool directly in-process without subprocess isolation.
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This method maintains conversation memory across calls, enabling proper
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testing of conversation functionality.
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Args:
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tool_name: Name of the tool to call (e.g., "precommit", "codereview")
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params: Parameters to pass to the tool
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Returns:
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tuple: (response_content, continuation_id) where continuation_id
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can be used for follow-up calls
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"""
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if self._tools is None:
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raise RuntimeError("Tools not imported. Call setUp() first.")
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if tool_name not in self._tools:
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raise ValueError(f"Tool '{tool_name}' not found. Available: {list(self._tools.keys())}")
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try:
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tool = self._tools[tool_name]
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self.logger.debug(f"Calling tool '{tool_name}' directly in-process")
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# Set up minimal model context if not provided
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if "model" not in params:
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params["model"] = "flash" # Use fast model for testing
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# Execute tool directly using asyncio
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loop = self._get_event_loop()
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# Import required modules for model resolution (similar to server.py)
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from config import DEFAULT_MODEL
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from providers.registry import ModelProviderRegistry
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from utils.model_context import ModelContext
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# Resolve model (simplified version of server.py logic)
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model_name = params.get("model", DEFAULT_MODEL)
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provider = ModelProviderRegistry.get_provider_for_model(model_name)
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if not provider:
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# Fallback to available model for testing
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available_models = list(ModelProviderRegistry.get_available_models(respect_restrictions=True).keys())
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if available_models:
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model_name = available_models[0]
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params["model"] = model_name
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self.logger.debug(f"Using fallback model for testing: {model_name}")
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# Create model context
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model_context = ModelContext(model_name)
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params["_model_context"] = model_context
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params["_resolved_model_name"] = model_name
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# Execute tool asynchronously
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result = loop.run_until_complete(tool.execute(params))
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if not result or len(result) == 0:
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return None, None
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# Extract response content
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response_text = result[0].text if hasattr(result[0], "text") else str(result[0])
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# Parse response to extract continuation_id
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continuation_id = self._extract_continuation_id_from_response(response_text)
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self.logger.debug(f"Tool '{tool_name}' completed successfully in-process")
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return response_text, continuation_id
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except Exception as e:
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self.logger.error(f"Direct tool call failed for '{tool_name}': {e}")
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return None, None
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def _extract_continuation_id_from_response(self, response_text: str) -> Optional[str]:
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"""Extract continuation_id from tool response"""
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try:
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# Parse the response as JSON to look for continuation metadata
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response_data = json.loads(response_text)
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# Look for continuation_id in various places
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if isinstance(response_data, dict):
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# Check metadata
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metadata = response_data.get("metadata", {})
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if "thread_id" in metadata:
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return metadata["thread_id"]
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# Check continuation_offer
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continuation_offer = response_data.get("continuation_offer", {})
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if continuation_offer and "continuation_id" in continuation_offer:
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return continuation_offer["continuation_id"]
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# Check follow_up_request
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follow_up = response_data.get("follow_up_request", {})
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if follow_up and "continuation_id" in follow_up:
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return follow_up["continuation_id"]
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return None
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except (json.JSONDecodeError, AttributeError):
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# If response is not JSON or doesn't have expected structure, return None
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return None
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def tearDown(self):
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"""Clean up after test"""
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super().cleanup_test_files()
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# Clear memory again for good measure
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self._clear_conversation_memory()
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@property
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def test_name(self) -> str:
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"""Get the test name"""
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return self.__class__.__name__
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@property
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def test_description(self) -> str:
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"""Get the test description"""
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return "In-process conversation test"
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