Module `adcp.types.base`

Classes

class AdCPBaseModel (**data: Any)

Expand source code

class AdCPBaseModel(BaseModel):
    """Base model for AdCP types with spec-compliant serialization.

    Defaults to ``extra='ignore'`` so that unknown fields from newer spec
    versions are silently dropped rather than causing validation errors.
    Generated types whose schemas set ``additionalProperties: true``
    override this with ``extra='allow'`` in their own ``model_config``.
    Consumers who want strict validation can override with ``extra='forbid'``.
    """

    model_config = ConfigDict(extra="ignore")

    def model_dump(self, **kwargs: Any) -> dict[str, Any]:
        if "exclude_none" not in kwargs:
            kwargs["exclude_none"] = True
        return super().model_dump(**kwargs)

    def model_dump_json(self, **kwargs: Any) -> str:
        if "exclude_none" not in kwargs:
            kwargs["exclude_none"] = True
        return super().model_dump_json(**kwargs)

    def model_summary(self) -> str:
        """Human-readable summary for protocol responses.

        Returns a standardized human-readable message suitable for MCP tool
        results, A2A task communications, and REST API responses.

        For types without a registered formatter, returns a generic message
        with the class name.
        """
        formatter = _RESPONSE_MESSAGE_REGISTRY.get(self.__class__.__name__)
        if formatter:
            return formatter(self)
        return f"{self.__class__.__name__} response"

Base model for AdCP types with spec-compliant serialization.

Defaults to extra='ignore' so that unknown fields from newer spec versions are silently dropped rather than causing validation errors. Generated types whose schemas set additionalProperties: true override this with extra='allow' in their own model_config. Consumers who want strict validation can override with extra='forbid'.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors

pydantic.main.BaseModel

Subclasses

Class variables

var model_config

Methods

def model_dump(self, **kwargs: Any) ‑> dict[str, typing.Any]

Expand source code

def model_dump(self, **kwargs: Any) -> dict[str, Any]:
    if "exclude_none" not in kwargs:
        kwargs["exclude_none"] = True
    return super().model_dump(**kwargs)

Usage Documentation

model_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Args

mode: The mode in which to_python should run. If mode is 'json', the output will only contain JSON serializable types. If mode is 'python', the output may contain non-JSON-serializable Python objects.
include: A set of fields to include in the output.
exclude: A set of fields to exclude from the output.
context: Additional context to pass to the serializer.
by_alias: Whether to use the field's alias in the dictionary key if defined.
exclude_unset: Whether to exclude fields that have not been explicitly set.
exclude_defaults: Whether to exclude fields that are set to their default value.
exclude_none: Whether to exclude fields that have a value of None.
exclude_computed_fields: Whether to exclude computed fields. While this can be useful for round-tripping, it is usually recommended to use the dedicated round_trip parameter instead.
round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors, "error" raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.
serialize_as_any: Whether to serialize fields with duck-typing serialization behavior.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.

Returns

A dictionary representation of the model.

def model_dump_json(self, **kwargs: Any) ‑> str

Expand source code

def model_dump_json(self, **kwargs: Any) -> str:
    if "exclude_none" not in kwargs:
        kwargs["exclude_none"] = True
    return super().model_dump_json(**kwargs)

Usage Documentation

model_dump_json

Generates a JSON representation of the model using Pydantic's to_json method.

Args

indent: Indentation to use in the JSON output. If None is passed, the output will be compact.
ensure_ascii: If True, the output is guaranteed to have all incoming non-ASCII characters escaped. If False (the default), these characters will be output as-is.
include: Field(s) to include in the JSON output.
exclude: Field(s) to exclude from the JSON output.
context: Additional context to pass to the serializer.
by_alias: Whether to serialize using field aliases.
exclude_unset: Whether to exclude fields that have not been explicitly set.
exclude_defaults: Whether to exclude fields that are set to their default value.
exclude_none: Whether to exclude fields that have a value of None.
exclude_computed_fields: Whether to exclude computed fields. While this can be useful for round-tripping, it is usually recommended to use the dedicated round_trip parameter instead.
round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors, "error" raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.
serialize_as_any: Whether to serialize fields with duck-typing serialization behavior.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.

Returns

A JSON string representation of the model.

def model_summary(self) ‑> str

Expand source code

def model_summary(self) -> str:
    """Human-readable summary for protocol responses.

    Returns a standardized human-readable message suitable for MCP tool
    results, A2A task communications, and REST API responses.

    For types without a registered formatter, returns a generic message
    with the class name.
    """
    formatter = _RESPONSE_MESSAGE_REGISTRY.get(self.__class__.__name__)
    if formatter:
        return formatter(self)
    return f"{self.__class__.__name__} response"

Human-readable summary for protocol responses.

Returns a standardized human-readable message suitable for MCP tool results, A2A task communications, and REST API responses.

For types without a registered formatter, returns a generic message with the class name.

class RegistryBaseModel (**data: Any)

Expand source code

class RegistryBaseModel(BaseModel):
    """Base model for registry API types.

    Uses ``extra='allow'`` so that new fields from the registry API
    are preserved rather than dropped. This differs from AdCPBaseModel
    which defaults to ``extra='ignore'`` for protocol types.
    """

    model_config = ConfigDict(extra="allow")

Base model for registry API types.

Uses extra='allow' so that new fields from the registry API are preserved rather than dropped. This differs from AdCPBaseModel which defaults to extra='ignore' for protocol types.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors

pydantic.main.BaseModel

Subclasses

Class variables

var model_config