MCP Config Manager

dnallm.mcp.config_manager

Configuration Manager for MCP Server.

This module provides configuration management functionality for the MCP server, including loading, validating, and managing both main server configurations and individual model configurations.

Classes

MCPConfigManager

MCPConfigManager(
    config_dir, server_config_file="mcp_server_config.yaml"
)

Manages MCP server configurations and model configurations.

Initialize the configuration manager.

Parameters:

Name	Type	Description	Default
config_dir	str	Path to the configuration directory	required
server_config_file	str	Name of the server configuration file	'mcp_server_config.yaml'

Source code in dnallm/mcp/config_manager.py

def __init__(
    self,
    config_dir: str,
    server_config_file: str = "mcp_server_config.yaml",
):
    """Initialize the configuration manager.

    Args:
        config_dir: Path to the configuration directory
        server_config_file: Name of the server configuration file
    """
    self.config_dir = Path(config_dir)
    self.server_config_path = self.config_dir / server_config_file
    self.server_config: MCPServerConfig | None = None
    self.model_configs: dict[str, InferenceModelConfig] = {}
    self._load_configurations()

Functions

get_enabled_models

get_enabled_models()

Get list of enabled model names.

Returns:

Type	Description
list[str]	List of enabled model names

Source code in dnallm/mcp/config_manager.py

def get_enabled_models(self) -> list[str]:
    """Get list of enabled model names.

    Returns:
        List of enabled model names
    """
    if not self.server_config:
        return []

    return [
        name
        for name, entry in self.server_config.models.items()
        if entry.enabled
    ]

get_logging_config

get_logging_config()

Get logging configuration.

Returns:

Type	Description
dict[str, Any]	Dictionary of logging configuration parameters

Source code in dnallm/mcp/config_manager.py

def get_logging_config(self) -> dict[str, Any]:
    """Get logging configuration.

    Returns:
        Dictionary of logging configuration parameters
    """
    if not self.server_config:
        return {}

    logging_config = self.server_config.logging
    return {
        "level": logging_config.level,
        "format": logging_config.format,
        "file": logging_config.file,
        "max_size": logging_config.max_size,
        "backup_count": logging_config.backup_count,
    }

get_model_config

get_model_config(model_name)

Get configuration for a specific model.

Parameters:

Name	Type	Description	Default
model_name	str	Name of the model	required

Returns:

Type	Description
InferenceModelConfig | None	InferenceModelConfig object or None if not found

Source code in dnallm/mcp/config_manager.py

def get_model_config(self, model_name: str) -> InferenceModelConfig | None:
    """Get configuration for a specific model.

    Args:
        model_name: Name of the model

    Returns:
        InferenceModelConfig object or None if not found
    """
    return self.model_configs.get(model_name)

get_model_configs

get_model_configs()

Get all loaded model configurations.

Returns:

Type	Description
dict[str, InferenceModelConfig]	Dictionary of model configurations

Source code in dnallm/mcp/config_manager.py

def get_model_configs(self) -> dict[str, InferenceModelConfig]:
    """Get all loaded model configurations.

    Returns:
        Dictionary of model configurations
    """
    return self.model_configs.copy()

get_model_info_summary

get_model_info_summary()

Get summary information about all loaded models.

Returns:

Type	Description
dict[str, Any]	Dictionary containing model information summary

Source code in dnallm/mcp/config_manager.py

def get_model_info_summary(self) -> dict[str, Any]:
    """Get summary information about all loaded models.

    Returns:
        Dictionary containing model information summary
    """
    summary = {
        "total_models": len(self.model_configs),
        "enabled_models": len(self.get_enabled_models()),
        "models": {},
    }

    for model_name, model_config in self.model_configs.items():
        summary["models"][model_name] = {
            "task_type": model_config.task.task_type,
            "num_labels": model_config.task.num_labels,
            "label_names": model_config.task.label_names,
            "model_path": model_config.model.path,
            "model_source": model_config.model.source,
            "architecture": model_config.model.task_info.architecture,
            "tokenizer": model_config.model.task_info.tokenizer,
            "species": model_config.model.task_info.species,
            "task_category": model_config.model.task_info.task_category,
        }

    return summary

get_model_priority

get_model_priority(model_name)

Get priority of a specific model.

Parameters:

Name	Type	Description	Default
model_name	str	Name of the model	required

Returns:

Type	Description
int	Priority value (1-10, higher is more important)

Source code in dnallm/mcp/config_manager.py

def get_model_priority(self, model_name: str) -> int:
    """Get priority of a specific model.

    Args:
        model_name: Name of the model

    Returns:
        Priority value (1-10, higher is more important)
    """
    if (
        not self.server_config
        or model_name not in self.server_config.models
    ):
        return 1

    return self.server_config.models[model_name].priority

get_multi_model_configs

get_multi_model_configs()

Get multi-model parallel prediction configurations.

Returns:

Type	Description
dict[str, Any]	Dictionary of multi-model configurations

Source code in dnallm/mcp/config_manager.py

def get_multi_model_configs(self) -> dict[str, Any]:
    """Get multi-model parallel prediction configurations.

    Returns:
        Dictionary of multi-model configurations
    """
    if not self.server_config:
        return {}

    return {
        name: {
            "name": config.name,
            "description": config.description,
            "models": config.models,
            "enabled": config.enabled,
        }
        for name, config in self.server_config.multi_model.items()
    }

get_server_config

get_server_config()

Get the main server configuration.

Returns:

Type	Description
MCPServerConfig | None	MCPServerConfig object or None if not loaded

Source code in dnallm/mcp/config_manager.py

def get_server_config(self) -> MCPServerConfig | None:
    """Get the main server configuration.

    Returns:
        MCPServerConfig object or None if not loaded
    """
    return self.server_config

get_sse_config

get_sse_config()

Get SSE configuration.

Returns:

Type	Description
dict[str, Any]	Dictionary of SSE configuration parameters

Source code in dnallm/mcp/config_manager.py

def get_sse_config(self) -> dict[str, Any]:
    """Get SSE configuration.

    Returns:
        Dictionary of SSE configuration parameters
    """
    if not self.server_config:
        return {}

    sse_config = self.server_config.sse
    return {
        "heartbeat_interval": sse_config.heartbeat_interval,
        "max_connections": sse_config.max_connections,
        "connection_timeout": sse_config.connection_timeout,
        "enable_compression": sse_config.enable_compression,
    }

reload_configurations

reload_configurations()

Reload all configurations from files.

Source code in dnallm/mcp/config_manager.py

def reload_configurations(self) -> None:
    """Reload all configurations from files."""
    logger.info("Reloading configurations...")
    self.model_configs.clear()
    self._load_configurations()
    logger.info("Configurations reloaded successfully")

validate_model_references

validate_model_references()

Validate that all model references are valid.

Returns:

Type	Description
list[str]	List of validation error messages

Source code in dnallm/mcp/config_manager.py

def validate_model_references(self) -> list[str]:
    """Validate that all model references are valid.

    Returns:
        List of validation error messages
    """
    errors = []

    if not self.server_config:
        errors.append("Server configuration not loaded")
        return errors

    # Check that all multi-model configurations reference existing models
    available_models = set(self.server_config.models.keys())

    for multi_name, multi_config in self.server_config.multi_model.items():
        for model_name in multi_config.models:
            if model_name not in available_models:
                errors.append(
                    f"Multi-model config '{multi_name}' references "
                    f"non-existent model '{model_name}'"
                )

    # Check that all model config files exist and are valid
    for model_name, _model_entry in self.server_config.models.items():
        if model_name not in self.model_configs:
            errors.append(
                f"Model configuration not loaded for '{model_name}'"
            )

    return errors

Functions