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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Beehive Innovations
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GitHub
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commit
4151c3c3a5
74
server.py
74
server.py
@@ -25,12 +25,21 @@ import sys
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import time
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from datetime import datetime
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from logging.handlers import RotatingFileHandler
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from pathlib import Path
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from typing import Any, Optional
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from mcp.server import Server
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from mcp.server.models import InitializationOptions
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from mcp.server.stdio import stdio_server
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from mcp.types import (
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from dotenv import load_dotenv
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# Load environment variables from .env file in the script's directory
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# This ensures .env is loaded regardless of the current working directory
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script_dir = Path(__file__).parent
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env_file = script_dir / ".env"
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load_dotenv(dotenv_path=env_file)
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from mcp.server import Server # noqa: E402
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from mcp.server.models import InitializationOptions # noqa: E402
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from mcp.server.stdio import stdio_server # noqa: E402
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from mcp.types import ( # noqa: E402
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GetPromptResult,
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Prompt,
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PromptMessage,
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@@ -41,13 +50,13 @@ from mcp.types import (
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ToolsCapability,
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)
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from config import (
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from config import ( # noqa: E402
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DEFAULT_MODEL,
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__author__,
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__updated__,
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__version__,
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)
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from tools import (
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from tools import ( # noqa: E402
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AnalyzeTool,
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ChatTool,
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CodeReviewTool,
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@@ -61,7 +70,7 @@ from tools import (
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ThinkDeepTool,
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TracerTool,
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)
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from tools.models import ToolOutput
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from tools.models import ToolOutput # noqa: E402
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# Configure logging for server operations
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# Can be controlled via LOG_LEVEL environment variable (DEBUG, INFO, WARNING, ERROR)
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@@ -101,13 +110,17 @@ root_logger.addHandler(stderr_handler)
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# Set root logger level
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root_logger.setLevel(getattr(logging, log_level, logging.INFO))
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# Add rotating file handler for Docker log monitoring
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# Add rotating file handler for local log monitoring
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try:
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# Create logs directory in project root
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log_dir = Path(__file__).parent / "logs"
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log_dir.mkdir(exist_ok=True)
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# Main server log with size-based rotation (20MB max per file)
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# This ensures logs don't grow indefinitely and are properly managed
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file_handler = RotatingFileHandler(
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"/tmp/mcp_server.log",
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log_dir / "mcp_server.log",
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maxBytes=20 * 1024 * 1024, # 20MB max file size
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backupCount=10, # Keep 10 rotated files (200MB total)
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encoding="utf-8",
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@@ -119,7 +132,7 @@ try:
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# Create a special logger for MCP activity tracking with size-based rotation
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mcp_logger = logging.getLogger("mcp_activity")
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mcp_file_handler = RotatingFileHandler(
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"/tmp/mcp_activity.log",
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log_dir / "mcp_activity.log",
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maxBytes=20 * 1024 * 1024, # 20MB max file size
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backupCount=5, # Keep 5 rotated files (100MB total)
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encoding="utf-8",
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@@ -131,16 +144,9 @@ try:
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# Ensure MCP activity also goes to stderr
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mcp_logger.propagate = True
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# Also keep a size-based rotation as backup (100MB max per file)
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# This prevents any single day's log from growing too large
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size_handler = RotatingFileHandler(
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"/tmp/mcp_server_overflow.log",
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maxBytes=100 * 1024 * 1024,
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backupCount=3, # 100MB
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)
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size_handler.setLevel(logging.WARNING) # Only warnings and errors
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size_handler.setFormatter(LocalTimeFormatter(log_format))
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logging.getLogger().addHandler(size_handler)
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# Log setup info directly to root logger since logger isn't defined yet
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logging.info(f"Logging to: {log_dir / 'mcp_server.log'}")
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logging.info(f"Process PID: {os.getpid()}")
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except Exception as e:
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print(f"Warning: Could not set up file logging: {e}", file=sys.stderr)
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@@ -243,7 +249,7 @@ def configure_providers():
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from providers.base import ProviderType
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from providers.custom import CustomProvider
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from providers.gemini import GeminiModelProvider
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from providers.openai import OpenAIModelProvider
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from providers.openai_provider import OpenAIModelProvider
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from providers.openrouter import OpenRouterProvider
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from providers.xai import XAIModelProvider
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from utils.model_restrictions import get_restriction_service
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@@ -450,7 +456,7 @@ async def handle_call_tool(name: str, arguments: dict[str, Any]) -> list[TextCon
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This function serves as the central orchestrator for multi-turn AI-to-AI conversations:
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1. THREAD RESUMPTION: When continuation_id is present, it reconstructs complete conversation
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context from Redis including conversation history and file references
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context from in-memory storage including conversation history and file references
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2. CROSS-TOOL CONTINUATION: Enables seamless handoffs between different tools (analyze →
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codereview → debug) while preserving full conversation context and file references
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@@ -465,7 +471,7 @@ async def handle_call_tool(name: str, arguments: dict[str, Any]) -> list[TextCon
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STATELESS TO STATEFUL BRIDGE:
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The MCP protocol is inherently stateless, but this function bridges the gap by:
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- Loading persistent conversation state from Redis
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- Loading persistent conversation state from in-memory storage
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- Reconstructing full multi-turn context for tool execution
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- Enabling tools to access previous exchanges and file references
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- Supporting conversation chains across different tool types
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@@ -700,13 +706,13 @@ async def reconstruct_thread_context(arguments: dict[str, Any]) -> dict[str, Any
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Reconstruct conversation context for stateless-to-stateful thread continuation.
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This is a critical function that transforms the inherently stateless MCP protocol into
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stateful multi-turn conversations. It loads persistent conversation state from Redis
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and rebuilds complete conversation context using the sophisticated dual prioritization
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stateful multi-turn conversations. It loads persistent conversation state from in-memory
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storage and rebuilds complete conversation context using the sophisticated dual prioritization
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strategy implemented in the conversation memory system.
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CONTEXT RECONSTRUCTION PROCESS:
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1. THREAD RETRIEVAL: Loads complete ThreadContext from Redis using continuation_id
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1. THREAD RETRIEVAL: Loads complete ThreadContext from storage using continuation_id
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- Includes all conversation turns with tool attribution
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- Preserves file references and cross-tool context
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- Handles conversation chains across multiple linked threads
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@@ -742,7 +748,7 @@ async def reconstruct_thread_context(arguments: dict[str, Any]) -> dict[str, Any
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ERROR HANDLING & RECOVERY:
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- Thread expiration: Provides clear instructions for conversation restart
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- Redis unavailability: Graceful degradation with error messaging
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- Storage unavailability: Graceful degradation with error messaging
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- Invalid continuation_id: Security validation and user-friendly errors
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Args:
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@@ -762,7 +768,7 @@ async def reconstruct_thread_context(arguments: dict[str, Any]) -> dict[str, Any
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Includes user-friendly recovery instructions
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Performance Characteristics:
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- O(1) thread lookup in Redis
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- O(1) thread lookup in memory
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- O(n) conversation history reconstruction where n = number of turns
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- Intelligent token budgeting prevents context window overflow
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- Optimized file deduplication minimizes redundant content
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@@ -778,12 +784,12 @@ async def reconstruct_thread_context(arguments: dict[str, Any]) -> dict[str, Any
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continuation_id = arguments["continuation_id"]
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# Get thread context from Redis
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logger.debug(f"[CONVERSATION_DEBUG] Looking up thread {continuation_id} in Redis")
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# Get thread context from storage
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logger.debug(f"[CONVERSATION_DEBUG] Looking up thread {continuation_id} in storage")
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context = get_thread(continuation_id)
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if not context:
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logger.warning(f"Thread not found: {continuation_id}")
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logger.debug(f"[CONVERSATION_DEBUG] Thread {continuation_id} not found in Redis or expired")
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logger.debug(f"[CONVERSATION_DEBUG] Thread {continuation_id} not found in storage or expired")
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# Log to activity file for monitoring
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try:
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@@ -795,8 +801,8 @@ async def reconstruct_thread_context(arguments: dict[str, Any]) -> dict[str, Any
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# Return error asking Claude to restart conversation with full context
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raise ValueError(
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f"Conversation thread '{continuation_id}' was not found or has expired. "
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f"This may happen if the conversation was created more than 1 hour ago or if there was an issue "
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f"with Redis storage. "
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f"This may happen if the conversation was created more than 3 hours ago or if the "
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f"server was restarted. "
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f"Please restart the conversation by providing your full question/prompt without the "
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f"continuation_id parameter. "
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f"This will create a new conversation thread that can continue with follow-up exchanges."
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@@ -1165,7 +1171,7 @@ async def main():
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# Validate and configure providers based on available API keys
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configure_providers()
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# Log startup message for Docker log monitoring
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# Log startup message
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logger.info("Zen MCP Server starting up...")
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logger.info(f"Log level: {log_level}")
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