ModelCore

Core utilities for ModelToolbox: logging, telemetry, configuration, and plugin system.

Features

• Unified Logging: Zero-configuration structured logging with console and file outputs
• Performance Monitoring: Decorators for tracking command execution time and errors
• Health Checks: Dependency checking and version information reporting
• MCP Server Support: Specialized logging and monitoring for MCP servers
• Plugin System: Dynamic plugin loading via entry points

Installation

pip install modeltoolbox-core

For development:

pip install -e "ModelCore[dev]"

Usage

Logging

from modeltoolbox_core.logging import get_logger

logger = get_logger(__name__)
logger.info("Processing started")
logger.error("An error occurred", exc_info=True)

Logs are written to:

• Console: INFO level and above
• File: ~/.modeltoolbox/logs/modeltoolbox-YYYY-MM-DD.log (all levels, JSON format)
• Errors: ~/.modeltoolbox/logs/errors-YYYY-MM-DD.log (ERROR level only)

Performance Tracking

from modeltoolbox_core.telemetry import track_performance

@track_performance
def expensive_operation():
    # Your code here
    pass

Health Checks

from modeltoolbox_core.health import health_check

status = health_check()
print(status)
# {
#   "status": "healthy",
#   "version_info": {"python": "3.11.0", ...},
#   "dependencies": {"git": {"available": True, "version": "..."}, ...}
# }

MCP Server Integration

from mcp.server.fastmcp import FastMCP
from modeltoolbox_core.mcp_logging import setup_mcp_logger, log_tool_call

logger = setup_mcp_logger("my_server")
mcp = FastMCP("my_server")

@mcp.tool()
@log_tool_call
def my_tool(param: str) -> dict:
    """My tool with automatic logging."""
    return {"result": param}

Testing

pytest ModelCore/tests/

License

MIT