import inspect import json from collections.abc import Awaitable, Callable, Sequence from itertools import chain from types import GenericAlias from typing import Annotated, Any, ForwardRef, cast, get_args, get_origin, get_type_hints import pydantic_core from pydantic import ( BaseModel, ConfigDict, Field, RootModel, WithJsonSchema, create_model, ) from pydantic._internal._typing_extra import eval_type_backport from pydantic.fields import FieldInfo from pydantic.json_schema import GenerateJsonSchema, JsonSchemaWarningKind from pydantic_core import PydanticUndefined from mcp.server.fastmcp.exceptions import InvalidSignature from mcp.server.fastmcp.utilities.logging import get_logger from mcp.server.fastmcp.utilities.types import Image from mcp.types import ContentBlock, TextContent logger = get_logger(__name__) class StrictJsonSchema(GenerateJsonSchema): """A JSON schema generator that raises exceptions instead of emitting warnings. This is used to detect non-serializable types during schema generation. """ def emit_warning(self, kind: JsonSchemaWarningKind, detail: str) -> None: # Raise an exception instead of emitting a warning raise ValueError(f"JSON schema warning: {kind} - {detail}") class ArgModelBase(BaseModel): """A model representing the arguments to a function.""" def model_dump_one_level(self) -> dict[str, Any]: """Return a dict of the model's fields, one level deep. That is, sub-models etc are not dumped - they are kept as pydantic models. """ kwargs: dict[str, Any] = {} for field_name, field_info in self.__class__.model_fields.items(): value = getattr(self, field_name) # Use the alias if it exists, otherwise use the field name output_name = field_info.alias if field_info.alias else field_name kwargs[output_name] = value return kwargs model_config = ConfigDict( arbitrary_types_allowed=True, ) class FuncMetadata(BaseModel): arg_model: Annotated[type[ArgModelBase], WithJsonSchema(None)] output_schema: dict[str, Any] | None = None output_model: Annotated[type[BaseModel], WithJsonSchema(None)] | None = None wrap_output: bool = False async def call_fn_with_arg_validation( self, fn: Callable[..., Any | Awaitable[Any]], fn_is_async: bool, arguments_to_validate: dict[str, Any], arguments_to_pass_directly: dict[str, Any] | None, ) -> Any: """Call the given function with arguments validated and injected. Arguments are first attempted to be parsed from JSON, then validated against the argument model, before being passed to the function. """ arguments_pre_parsed = self.pre_parse_json(arguments_to_validate) arguments_parsed_model = self.arg_model.model_validate(arguments_pre_parsed) arguments_parsed_dict = arguments_parsed_model.model_dump_one_level() arguments_parsed_dict |= arguments_to_pass_directly or {} if fn_is_async: return await fn(**arguments_parsed_dict) else: return fn(**arguments_parsed_dict) def convert_result(self, result: Any) -> Any: """ Convert the result of a function call to the appropriate format for the lowlevel server tool call handler: - If output_model is None, return the unstructured content directly. - If output_model is not None, convert the result to structured output format (dict[str, Any]) and return both unstructured and structured content. Note: we return unstructured content here **even though the lowlevel server tool call handler provides generic backwards compatibility serialization of structured content**. This is for FastMCP backwards compatibility: we need to retain FastMCP's ad hoc conversion logic for constructing unstructured output from function return values, whereas the lowlevel server simply serializes the structured output. """ unstructured_content = _convert_to_content(result) if self.output_schema is None: return unstructured_content else: if self.wrap_output: result = {"result": result} assert self.output_model is not None, "Output model must be set if output schema is defined" validated = self.output_model.model_validate(result) structured_content = validated.model_dump(mode="json", by_alias=True) return (unstructured_content, structured_content) def pre_parse_json(self, data: dict[str, Any]) -> dict[str, Any]: """Pre-parse data from JSON. Return a dict with same keys as input but with values parsed from JSON if appropriate. This is to handle cases like `["a", "b", "c"]` being passed in as JSON inside a string rather than an actual list. Claude desktop is prone to this - in fact it seems incapable of NOT doing this. For sub-models, it tends to pass dicts (JSON objects) as JSON strings, which can be pre-parsed here. """ new_data = data.copy() # Shallow copy # Build a mapping from input keys (including aliases) to field info key_to_field_info: dict[str, FieldInfo] = {} for field_name, field_info in self.arg_model.model_fields.items(): # Map both the field name and its alias (if any) to the field info key_to_field_info[field_name] = field_info if field_info.alias: key_to_field_info[field_info.alias] = field_info for data_key in data.keys(): if data_key not in key_to_field_info: continue field_info = key_to_field_info[data_key] if isinstance(data[data_key], str) and field_info.annotation is not str: try: pre_parsed = json.loads(data[data_key]) except json.JSONDecodeError: continue # Not JSON - skip if isinstance(pre_parsed, str | int | float): # This is likely that the raw value is e.g. `"hello"` which we # Should really be parsed as '"hello"' in Python - but if we parse # it as JSON it'll turn into just 'hello'. So we skip it. continue new_data[data_key] = pre_parsed assert new_data.keys() == data.keys() return new_data model_config = ConfigDict( arbitrary_types_allowed=True, ) def func_metadata( func: Callable[..., Any], skip_names: Sequence[str] = (), structured_output: bool | None = None, ) -> FuncMetadata: """Given a function, return metadata including a pydantic model representing its signature. The use case for this is ``` meta = func_metadata(func) validated_args = meta.arg_model.model_validate(some_raw_data_dict) return func(**validated_args.model_dump_one_level()) ``` **critically** it also provides pre-parse helper to attempt to parse things from JSON. Args: func: The function to convert to a pydantic model skip_names: A list of parameter names to skip. These will not be included in the model. structured_output: Controls whether the tool's output is structured or unstructured - If None, auto-detects based on the function's return type annotation - If True, unconditionally creates a structured tool (return type annotation permitting) - If False, unconditionally creates an unstructured tool If structured, creates a Pydantic model for the function's result based on its annotation. Supports various return types: - BaseModel subclasses (used directly) - Primitive types (str, int, float, bool, bytes, None) - wrapped in a model with a 'result' field - TypedDict - converted to a Pydantic model with same fields - Dataclasses and other annotated classes - converted to Pydantic models - Generic types (list, dict, Union, etc.) - wrapped in a model with a 'result' field Returns: A FuncMetadata object containing: - arg_model: A pydantic model representing the function's arguments - output_model: A pydantic model for the return type if output is structured - output_conversion: Records how function output should be converted before returning. """ sig = _get_typed_signature(func) params = sig.parameters dynamic_pydantic_model_params: dict[str, Any] = {} globalns = getattr(func, "__globals__", {}) for param in params.values(): if param.name.startswith("_"): raise InvalidSignature(f"Parameter {param.name} of {func.__name__} cannot start with '_'") if param.name in skip_names: continue annotation = param.annotation # `x: None` / `x: None = None` if annotation is None: annotation = Annotated[ None, Field(default=param.default if param.default is not inspect.Parameter.empty else PydanticUndefined), ] # Untyped field if annotation is inspect.Parameter.empty: annotation = Annotated[ Any, Field(), # 🤷 WithJsonSchema({"title": param.name, "type": "string"}), ] field_info = FieldInfo.from_annotated_attribute( _get_typed_annotation(annotation, globalns), param.default if param.default is not inspect.Parameter.empty else PydanticUndefined, ) # Check if the parameter name conflicts with BaseModel attributes # This is necessary because Pydantic warns about shadowing parent attributes if hasattr(BaseModel, param.name) and callable(getattr(BaseModel, param.name)): # Use an alias to avoid the shadowing warning field_info.alias = param.name field_info.validation_alias = param.name field_info.serialization_alias = param.name # Use a prefixed internal name internal_name = f"field_{param.name}" dynamic_pydantic_model_params[internal_name] = (field_info.annotation, field_info) else: dynamic_pydantic_model_params[param.name] = (field_info.annotation, field_info) continue arguments_model = create_model( f"{func.__name__}Arguments", **dynamic_pydantic_model_params, __base__=ArgModelBase, ) if structured_output is False: return FuncMetadata(arg_model=arguments_model) # set up structured output support based on return type annotation if sig.return_annotation is inspect.Parameter.empty and structured_output is True: raise InvalidSignature(f"Function {func.__name__}: return annotation required for structured output") output_info = FieldInfo.from_annotation(_get_typed_annotation(sig.return_annotation, globalns)) annotation = output_info.annotation output_model, output_schema, wrap_output = _try_create_model_and_schema(annotation, func.__name__, output_info) if output_model is None and structured_output is True: # Model creation failed or produced warnings - no structured output raise InvalidSignature( f"Function {func.__name__}: return type {annotation} is not serializable for structured output" ) return FuncMetadata( arg_model=arguments_model, output_schema=output_schema, output_model=output_model, wrap_output=wrap_output, ) def _try_create_model_and_schema( annotation: Any, func_name: str, field_info: FieldInfo ) -> tuple[type[BaseModel] | None, dict[str, Any] | None, bool]: """Try to create a model and schema for the given annotation without warnings. Returns: tuple of (model or None, schema or None, wrap_output) Model and schema are None if warnings occur or creation fails. wrap_output is True if the result needs to be wrapped in {"result": ...} """ model = None wrap_output = False # First handle special case: None if annotation is None: model = _create_wrapped_model(func_name, annotation, field_info) wrap_output = True # Handle GenericAlias types (list[str], dict[str, int], Union[str, int], etc.) elif isinstance(annotation, GenericAlias): origin = get_origin(annotation) # Special case: dict with string keys can use RootModel if origin is dict: args = get_args(annotation) if len(args) == 2 and args[0] is str: model = _create_dict_model(func_name, annotation) else: # dict with non-str keys needs wrapping model = _create_wrapped_model(func_name, annotation, field_info) wrap_output = True else: # All other generic types need wrapping (list, tuple, Union, Optional, etc.) model = _create_wrapped_model(func_name, annotation, field_info) wrap_output = True # Handle regular type objects elif isinstance(annotation, type): type_annotation: type[Any] = cast(type[Any], annotation) # Case 1: BaseModel subclasses (can be used directly) if issubclass(annotation, BaseModel): model = annotation # Case 2: TypedDict (special dict subclass with __annotations__) elif hasattr(type_annotation, "__annotations__") and issubclass(annotation, dict): model = _create_model_from_typeddict(type_annotation) # Case 3: Primitive types that need wrapping elif annotation in (str, int, float, bool, bytes, type(None)): model = _create_wrapped_model(func_name, annotation, field_info) wrap_output = True # Case 4: Other class types (dataclasses, regular classes with annotations) else: type_hints = get_type_hints(type_annotation) if type_hints: # Classes with type hints can be converted to Pydantic models model = _create_model_from_class(type_annotation) # Classes without type hints are not serializable - model remains None # Handle any other types not covered above else: # This includes typing constructs that aren't GenericAlias in Python 3.10 # (e.g., Union, Optional in some Python versions) model = _create_wrapped_model(func_name, annotation, field_info) wrap_output = True if model: # If we successfully created a model, try to get its schema # Use StrictJsonSchema to raise exceptions instead of warnings try: schema = model.model_json_schema(schema_generator=StrictJsonSchema) except (TypeError, ValueError, pydantic_core.SchemaError, pydantic_core.ValidationError) as e: # These are expected errors when a type can't be converted to a Pydantic schema # TypeError: When Pydantic can't handle the type # ValueError: When there are issues with the type definition (including our custom warnings) # SchemaError: When Pydantic can't build a schema # ValidationError: When validation fails logger.info(f"Cannot create schema for type {annotation} in {func_name}: {type(e).__name__}: {e}") return None, None, False return model, schema, wrap_output return None, None, False def _create_model_from_class(cls: type[Any]) -> type[BaseModel]: """Create a Pydantic model from an ordinary class. The created model will: - Have the same name as the class - Have fields with the same names and types as the class's fields - Include all fields whose type does not include None in the set of required fields Precondition: cls must have type hints (i.e., get_type_hints(cls) is non-empty) """ type_hints = get_type_hints(cls) model_fields: dict[str, Any] = {} for field_name, field_type in type_hints.items(): if field_name.startswith("_"): continue default = getattr(cls, field_name, PydanticUndefined) field_info = FieldInfo.from_annotated_attribute(field_type, default) model_fields[field_name] = (field_info.annotation, field_info) # Create a base class with the config class BaseWithConfig(BaseModel): model_config = ConfigDict(from_attributes=True) return create_model(cls.__name__, **model_fields, __base__=BaseWithConfig) def _create_model_from_typeddict(td_type: type[Any]) -> type[BaseModel]: """Create a Pydantic model from a TypedDict. The created model will have the same name and fields as the TypedDict. """ type_hints = get_type_hints(td_type) required_keys = getattr(td_type, "__required_keys__", set(type_hints.keys())) model_fields: dict[str, Any] = {} for field_name, field_type in type_hints.items(): field_info = FieldInfo.from_annotation(field_type) if field_name not in required_keys: # For optional TypedDict fields, set default=None # This makes them not required in the Pydantic model # The model should use exclude_unset=True when dumping to get TypedDict semantics field_info.default = None model_fields[field_name] = (field_info.annotation, field_info) return create_model(td_type.__name__, **model_fields, __base__=BaseModel) def _create_wrapped_model(func_name: str, annotation: Any, field_info: FieldInfo) -> type[BaseModel]: """Create a model that wraps a type in a 'result' field. This is used for primitive types, generic types like list/dict, etc. """ model_name = f"{func_name}Output" # Pydantic needs type(None) instead of None for the type annotation if annotation is None: annotation = type(None) return create_model(model_name, result=(annotation, field_info), __base__=BaseModel) def _create_dict_model(func_name: str, dict_annotation: Any) -> type[BaseModel]: """Create a RootModel for dict[str, T] types.""" class DictModel(RootModel[dict_annotation]): pass # Give it a meaningful name DictModel.__name__ = f"{func_name}DictOutput" DictModel.__qualname__ = f"{func_name}DictOutput" return DictModel def _get_typed_annotation(annotation: Any, globalns: dict[str, Any]) -> Any: def try_eval_type(value: Any, globalns: dict[str, Any], localns: dict[str, Any]) -> tuple[Any, bool]: try: return eval_type_backport(value, globalns, localns), True except NameError: return value, False if isinstance(annotation, str): annotation = ForwardRef(annotation) annotation, status = try_eval_type(annotation, globalns, globalns) # This check and raise could perhaps be skipped, and we (FastMCP) just call # model_rebuild right before using it 🤷 if status is False: raise InvalidSignature(f"Unable to evaluate type annotation {annotation}") return annotation def _get_typed_signature(call: Callable[..., Any]) -> inspect.Signature: """Get function signature while evaluating forward references""" signature = inspect.signature(call) globalns = getattr(call, "__globals__", {}) typed_params = [ inspect.Parameter( name=param.name, kind=param.kind, default=param.default, annotation=_get_typed_annotation(param.annotation, globalns), ) for param in signature.parameters.values() ] typed_return = _get_typed_annotation(signature.return_annotation, globalns) typed_signature = inspect.Signature(typed_params, return_annotation=typed_return) return typed_signature def _convert_to_content( result: Any, ) -> Sequence[ContentBlock]: """ Convert a result to a sequence of content objects. Note: This conversion logic comes from previous versions of FastMCP and is being retained for purposes of backwards compatibility. It produces different unstructured output than the lowlevel server tool call handler, which just serializes structured content verbatim. """ if result is None: return [] if isinstance(result, ContentBlock): return [result] if isinstance(result, Image): return [result.to_image_content()] if isinstance(result, list | tuple): return list( chain.from_iterable( _convert_to_content(item) for item in result # type: ignore ) ) if not isinstance(result, str): result = pydantic_core.to_json(result, fallback=str, indent=2).decode() return [TextContent(type="text", text=result)]