chore: 添加虚拟环境到仓库

- 添加 backend_service/venv 虚拟环境
- 包含所有Python依赖包
- 注意：虚拟环境约393MB，包含12655个文件

backend_service/venv/lib/python3.13/site-packages/mcp/server/fastmcp/__init__.py

@@ -0,0 +1,11 @@
+"""FastMCP - A more ergonomic interface for MCP servers."""
+
+from importlib.metadata import version
+
+from mcp.types import Icon
+
+from .server import Context, FastMCP
+from .utilities.types import Audio, Image
+
+__version__ = version("mcp")
+__all__ = ["FastMCP", "Context", "Image", "Audio", "Icon"]

backend_service/venv/lib/python3.13/site-packages/mcp/server/fastmcp/exceptions.py

@@ -0,0 +1,21 @@
+"""Custom exceptions for FastMCP."""
+
+
+class FastMCPError(Exception):
+    """Base error for FastMCP."""
+
+
+class ValidationError(FastMCPError):
+    """Error in validating parameters or return values."""
+
+
+class ResourceError(FastMCPError):
+    """Error in resource operations."""
+
+
+class ToolError(FastMCPError):
+    """Error in tool operations."""
+
+
+class InvalidSignature(Exception):
+    """Invalid signature for use with FastMCP."""

backend_service/venv/lib/python3.13/site-packages/mcp/server/fastmcp/prompts/__init__.py

@@ -0,0 +1,4 @@
+from .base import Prompt
+from .manager import PromptManager
+
+__all__ = ["Prompt", "PromptManager"]

backend_service/venv/lib/python3.13/site-packages/mcp/server/fastmcp/prompts/base.py

@@ -0,0 +1,183 @@
+"""Base classes for FastMCP prompts."""
+
+from __future__ import annotations
+
+import inspect
+from collections.abc import Awaitable, Callable, Sequence
+from typing import TYPE_CHECKING, Any, Literal
+
+import pydantic_core
+from pydantic import BaseModel, Field, TypeAdapter, validate_call
+
+from mcp.server.fastmcp.utilities.context_injection import find_context_parameter, inject_context
+from mcp.server.fastmcp.utilities.func_metadata import func_metadata
+from mcp.types import ContentBlock, Icon, TextContent
+
+if TYPE_CHECKING:
+    from mcp.server.fastmcp.server import Context
+    from mcp.server.session import ServerSessionT
+    from mcp.shared.context import LifespanContextT, RequestT
+
+
+class Message(BaseModel):
+    """Base class for all prompt messages."""
+
+    role: Literal["user", "assistant"]
+    content: ContentBlock
+
+    def __init__(self, content: str | ContentBlock, **kwargs: Any):
+        if isinstance(content, str):
+            content = TextContent(type="text", text=content)
+        super().__init__(content=content, **kwargs)
+
+
+class UserMessage(Message):
+    """A message from the user."""
+
+    role: Literal["user", "assistant"] = "user"
+
+    def __init__(self, content: str | ContentBlock, **kwargs: Any):
+        super().__init__(content=content, **kwargs)
+
+
+class AssistantMessage(Message):
+    """A message from the assistant."""
+
+    role: Literal["user", "assistant"] = "assistant"
+
+    def __init__(self, content: str | ContentBlock, **kwargs: Any):
+        super().__init__(content=content, **kwargs)
+
+
+message_validator = TypeAdapter[UserMessage | AssistantMessage](UserMessage | AssistantMessage)
+
+SyncPromptResult = str | Message | dict[str, Any] | Sequence[str | Message | dict[str, Any]]
+PromptResult = SyncPromptResult | Awaitable[SyncPromptResult]
+
+
+class PromptArgument(BaseModel):
+    """An argument that can be passed to a prompt."""
+
+    name: str = Field(description="Name of the argument")
+    description: str | None = Field(None, description="Description of what the argument does")
+    required: bool = Field(default=False, description="Whether the argument is required")
+
+
+class Prompt(BaseModel):
+    """A prompt template that can be rendered with parameters."""
+
+    name: str = Field(description="Name of the prompt")
+    title: str | None = Field(None, description="Human-readable title of the prompt")
+    description: str | None = Field(None, description="Description of what the prompt does")
+    arguments: list[PromptArgument] | None = Field(None, description="Arguments that can be passed to the prompt")
+    fn: Callable[..., PromptResult | Awaitable[PromptResult]] = Field(exclude=True)
+    icons: list[Icon] | None = Field(default=None, description="Optional list of icons for this prompt")
+    context_kwarg: str | None = Field(None, description="Name of the kwarg that should receive context", exclude=True)
+
+    @classmethod
+    def from_function(
+        cls,
+        fn: Callable[..., PromptResult | Awaitable[PromptResult]],
+        name: str | None = None,
+        title: str | None = None,
+        description: str | None = None,
+        icons: list[Icon] | None = None,
+        context_kwarg: str | None = None,
+    ) -> Prompt:
+        """Create a Prompt from a function.
+
+        The function can return:
+        - A string (converted to a message)
+        - A Message object
+        - A dict (converted to a message)
+        - A sequence of any of the above
+        """
+        func_name = name or fn.__name__
+
+        if func_name == "<lambda>":  # pragma: no cover
+            raise ValueError("You must provide a name for lambda functions")
+
+        # Find context parameter if it exists
+        if context_kwarg is None:  # pragma: no branch
+            context_kwarg = find_context_parameter(fn)
+
+        # Get schema from func_metadata, excluding context parameter
+        func_arg_metadata = func_metadata(
+            fn,
+            skip_names=[context_kwarg] if context_kwarg is not None else [],
+        )
+        parameters = func_arg_metadata.arg_model.model_json_schema()
+
+        # Convert parameters to PromptArguments
+        arguments: list[PromptArgument] = []
+        if "properties" in parameters:  # pragma: no branch
+            for param_name, param in parameters["properties"].items():
+                required = param_name in parameters.get("required", [])
+                arguments.append(
+                    PromptArgument(
+                        name=param_name,
+                        description=param.get("description"),
+                        required=required,
+                    )
+                )
+
+        # ensure the arguments are properly cast
+        fn = validate_call(fn)
+
+        return cls(
+            name=func_name,
+            title=title,
+            description=description or fn.__doc__ or "",
+            arguments=arguments,
+            fn=fn,
+            icons=icons,
+            context_kwarg=context_kwarg,
+        )
+
+    async def render(
+        self,
+        arguments: dict[str, Any] | None = None,
+        context: Context[ServerSessionT, LifespanContextT, RequestT] | None = None,
+    ) -> list[Message]:
+        """Render the prompt with arguments."""
+        # Validate required arguments
+        if self.arguments:
+            required = {arg.name for arg in self.arguments if arg.required}
+            provided = set(arguments or {})
+            missing = required - provided
+            if missing:
+                raise ValueError(f"Missing required arguments: {missing}")
+
+        try:
+            # Add context to arguments if needed
+            call_args = inject_context(self.fn, arguments or {}, context, self.context_kwarg)
+
+            # Call function and check if result is a coroutine
+            result = self.fn(**call_args)
+            if inspect.iscoroutine(result):
+                result = await result
+
+            # Validate messages
+            if not isinstance(result, list | tuple):
+                result = [result]
+
+            # Convert result to messages
+            messages: list[Message] = []
+            for msg in result:  # type: ignore[reportUnknownVariableType]
+                try:
+                    if isinstance(msg, Message):
+                        messages.append(msg)
+                    elif isinstance(msg, dict):
+                        messages.append(message_validator.validate_python(msg))
+                    elif isinstance(msg, str):
+                        content = TextContent(type="text", text=msg)
+                        messages.append(UserMessage(content=content))
+                    else:  # pragma: no cover
+                        content = pydantic_core.to_json(msg, fallback=str, indent=2).decode()
+                        messages.append(Message(role="user", content=content))
+                except Exception:  # pragma: no cover
+                    raise ValueError(f"Could not convert prompt result to message: {msg}")
+
+            return messages
+        except Exception as e:  # pragma: no cover
+            raise ValueError(f"Error rendering prompt {self.name}: {e}")

backend_service/venv/lib/python3.13/site-packages/mcp/server/fastmcp/prompts/manager.py

@@ -0,0 +1,60 @@
+"""Prompt management functionality."""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any
+
+from mcp.server.fastmcp.prompts.base import Message, Prompt
+from mcp.server.fastmcp.utilities.logging import get_logger
+
+if TYPE_CHECKING:
+    from mcp.server.fastmcp.server import Context
+    from mcp.server.session import ServerSessionT
+    from mcp.shared.context import LifespanContextT, RequestT
+
+logger = get_logger(__name__)
+
+
+class PromptManager:
+    """Manages FastMCP prompts."""
+
+    def __init__(self, warn_on_duplicate_prompts: bool = True):
+        self._prompts: dict[str, Prompt] = {}
+        self.warn_on_duplicate_prompts = warn_on_duplicate_prompts
+
+    def get_prompt(self, name: str) -> Prompt | None:
+        """Get prompt by name."""
+        return self._prompts.get(name)
+
+    def list_prompts(self) -> list[Prompt]:
+        """List all registered prompts."""
+        return list(self._prompts.values())
+
+    def add_prompt(
+        self,
+        prompt: Prompt,
+    ) -> Prompt:
+        """Add a prompt to the manager."""
+
+        # Check for duplicates
+        existing = self._prompts.get(prompt.name)
+        if existing:
+            if self.warn_on_duplicate_prompts:
+                logger.warning(f"Prompt already exists: {prompt.name}")
+            return existing
+
+        self._prompts[prompt.name] = prompt
+        return prompt
+
+    async def render_prompt(
+        self,
+        name: str,
+        arguments: dict[str, Any] | None = None,
+        context: Context[ServerSessionT, LifespanContextT, RequestT] | None = None,
+    ) -> list[Message]:
+        """Render a prompt by name with arguments."""
+        prompt = self.get_prompt(name)
+        if not prompt:
+            raise ValueError(f"Unknown prompt: {name}")
+
+        return await prompt.render(arguments, context=context)

backend_service/venv/lib/python3.13/site-packages/mcp/server/fastmcp/resources/__init__.py

@@ -0,0 +1,23 @@
+from .base import Resource
+from .resource_manager import ResourceManager
+from .templates import ResourceTemplate
+from .types import (
+    BinaryResource,
+    DirectoryResource,
+    FileResource,
+    FunctionResource,
+    HttpResource,
+    TextResource,
+)
+
+__all__ = [
+    "Resource",
+    "TextResource",
+    "BinaryResource",
+    "FunctionResource",
+    "FileResource",
+    "HttpResource",
+    "DirectoryResource",
+    "ResourceTemplate",
+    "ResourceManager",
+]

backend_service/venv/lib/python3.13/site-packages/mcp/server/fastmcp/resources/base.py

@@ -0,0 +1,49 @@
+"""Base classes and interfaces for FastMCP resources."""
+
+import abc
+from typing import Annotated
+
+from pydantic import (
+    AnyUrl,
+    BaseModel,
+    ConfigDict,
+    Field,
+    UrlConstraints,
+    ValidationInfo,
+    field_validator,
+)
+
+from mcp.types import Annotations, Icon
+
+
+class Resource(BaseModel, abc.ABC):
+    """Base class for all resources."""
+
+    model_config = ConfigDict(validate_default=True)
+
+    uri: Annotated[AnyUrl, UrlConstraints(host_required=False)] = Field(default=..., description="URI of the resource")
+    name: str | None = Field(description="Name of the resource", default=None)
+    title: str | None = Field(description="Human-readable title of the resource", default=None)
+    description: str | None = Field(description="Description of the resource", default=None)
+    mime_type: str = Field(
+        default="text/plain",
+        description="MIME type of the resource content",
+        pattern=r"^[a-zA-Z0-9]+/[a-zA-Z0-9\-+.]+$",
+    )
+    icons: list[Icon] | None = Field(default=None, description="Optional list of icons for this resource")
+    annotations: Annotations | None = Field(default=None, description="Optional annotations for the resource")
+
+    @field_validator("name", mode="before")
+    @classmethod
+    def set_default_name(cls, name: str | None, info: ValidationInfo) -> str:
+        """Set default name from URI if not provided."""
+        if name:
+            return name
+        if uri := info.data.get("uri"):
+            return str(uri)
+        raise ValueError("Either name or uri must be provided")
+
+    @abc.abstractmethod
+    async def read(self) -> str | bytes:
+        """Read the resource content."""
+        pass  # pragma: no cover

backend_service/venv/lib/python3.13/site-packages/mcp/server/fastmcp/resources/resource_manager.py

@@ -0,0 +1,113 @@
+"""Resource manager functionality."""
+
+from __future__ import annotations
+
+from collections.abc import Callable
+from typing import TYPE_CHECKING, Any
+
+from pydantic import AnyUrl
+
+from mcp.server.fastmcp.resources.base import Resource
+from mcp.server.fastmcp.resources.templates import ResourceTemplate
+from mcp.server.fastmcp.utilities.logging import get_logger
+from mcp.types import Annotations, Icon
+
+if TYPE_CHECKING:
+    from mcp.server.fastmcp.server import Context
+    from mcp.server.session import ServerSessionT
+    from mcp.shared.context import LifespanContextT, RequestT
+
+logger = get_logger(__name__)
+
+
+class ResourceManager:
+    """Manages FastMCP resources."""
+
+    def __init__(self, warn_on_duplicate_resources: bool = True):
+        self._resources: dict[str, Resource] = {}
+        self._templates: dict[str, ResourceTemplate] = {}
+        self.warn_on_duplicate_resources = warn_on_duplicate_resources
+
+    def add_resource(self, resource: Resource) -> Resource:
+        """Add a resource to the manager.
+
+        Args:
+            resource: A Resource instance to add
+
+        Returns:
+            The added resource. If a resource with the same URI already exists,
+            returns the existing resource.
+        """
+        logger.debug(
+            "Adding resource",
+            extra={
+                "uri": resource.uri,
+                "type": type(resource).__name__,
+                "resource_name": resource.name,
+            },
+        )
+        existing = self._resources.get(str(resource.uri))
+        if existing:
+            if self.warn_on_duplicate_resources:
+                logger.warning(f"Resource already exists: {resource.uri}")
+            return existing
+        self._resources[str(resource.uri)] = resource
+        return resource
+
+    def add_template(
+        self,
+        fn: Callable[..., Any],
+        uri_template: str,
+        name: str | None = None,
+        title: str | None = None,
+        description: str | None = None,
+        mime_type: str | None = None,
+        icons: list[Icon] | None = None,
+        annotations: Annotations | None = None,
+    ) -> ResourceTemplate:
+        """Add a template from a function."""
+        template = ResourceTemplate.from_function(
+            fn,
+            uri_template=uri_template,
+            name=name,
+            title=title,
+            description=description,
+            mime_type=mime_type,
+            icons=icons,
+            annotations=annotations,
+        )
+        self._templates[template.uri_template] = template
+        return template
+
+    async def get_resource(
+        self,
+        uri: AnyUrl | str,
+        context: Context[ServerSessionT, LifespanContextT, RequestT] | None = None,
+    ) -> Resource | None:
+        """Get resource by URI, checking concrete resources first, then templates."""
+        uri_str = str(uri)
+        logger.debug("Getting resource", extra={"uri": uri_str})
+
+        # First check concrete resources
+        if resource := self._resources.get(uri_str):
+            return resource
+
+        # Then check templates
+        for template in self._templates.values():
+            if params := template.matches(uri_str):
+                try:
+                    return await template.create_resource(uri_str, params, context=context)
+                except Exception as e:  # pragma: no cover
+                    raise ValueError(f"Error creating resource from template: {e}")
+
+        raise ValueError(f"Unknown resource: {uri}")
+
+    def list_resources(self) -> list[Resource]:
+        """List all registered resources."""
+        logger.debug("Listing resources", extra={"count": len(self._resources)})
+        return list(self._resources.values())
+
+    def list_templates(self) -> list[ResourceTemplate]:
+        """List all registered templates."""
+        logger.debug("Listing templates", extra={"count": len(self._templates)})
+        return list(self._templates.values())

backend_service/venv/lib/python3.13/site-packages/mcp/server/fastmcp/resources/templates.py

@@ -0,0 +1,118 @@
+"""Resource template functionality."""
+
+from __future__ import annotations
+
+import inspect
+import re
+from collections.abc import Callable
+from typing import TYPE_CHECKING, Any
+
+from pydantic import BaseModel, Field, validate_call
+
+from mcp.server.fastmcp.resources.types import FunctionResource, Resource
+from mcp.server.fastmcp.utilities.context_injection import find_context_parameter, inject_context
+from mcp.server.fastmcp.utilities.func_metadata import func_metadata
+from mcp.types import Annotations, Icon
+
+if TYPE_CHECKING:
+    from mcp.server.fastmcp.server import Context
+    from mcp.server.session import ServerSessionT
+    from mcp.shared.context import LifespanContextT, RequestT
+
+
+class ResourceTemplate(BaseModel):
+    """A template for dynamically creating resources."""
+
+    uri_template: str = Field(description="URI template with parameters (e.g. weather://{city}/current)")
+    name: str = Field(description="Name of the resource")
+    title: str | None = Field(description="Human-readable title of the resource", default=None)
+    description: str | None = Field(description="Description of what the resource does")
+    mime_type: str = Field(default="text/plain", description="MIME type of the resource content")
+    icons: list[Icon] | None = Field(default=None, description="Optional list of icons for the resource template")
+    annotations: Annotations | None = Field(default=None, description="Optional annotations for the resource template")
+    fn: Callable[..., Any] = Field(exclude=True)
+    parameters: dict[str, Any] = Field(description="JSON schema for function parameters")
+    context_kwarg: str | None = Field(None, description="Name of the kwarg that should receive context")
+
+    @classmethod
+    def from_function(
+        cls,
+        fn: Callable[..., Any],
+        uri_template: str,
+        name: str | None = None,
+        title: str | None = None,
+        description: str | None = None,
+        mime_type: str | None = None,
+        icons: list[Icon] | None = None,
+        annotations: Annotations | None = None,
+        context_kwarg: str | None = None,
+    ) -> ResourceTemplate:
+        """Create a template from a function."""
+        func_name = name or fn.__name__
+        if func_name == "<lambda>":
+            raise ValueError("You must provide a name for lambda functions")  # pragma: no cover
+
+        # Find context parameter if it exists
+        if context_kwarg is None:  # pragma: no branch
+            context_kwarg = find_context_parameter(fn)
+
+        # Get schema from func_metadata, excluding context parameter
+        func_arg_metadata = func_metadata(
+            fn,
+            skip_names=[context_kwarg] if context_kwarg is not None else [],
+        )
+        parameters = func_arg_metadata.arg_model.model_json_schema()
+
+        # ensure the arguments are properly cast
+        fn = validate_call(fn)
+
+        return cls(
+            uri_template=uri_template,
+            name=func_name,
+            title=title,
+            description=description or fn.__doc__ or "",
+            mime_type=mime_type or "text/plain",
+            icons=icons,
+            annotations=annotations,
+            fn=fn,
+            parameters=parameters,
+            context_kwarg=context_kwarg,
+        )
+
+    def matches(self, uri: str) -> dict[str, Any] | None:
+        """Check if URI matches template and extract parameters."""
+        # Convert template to regex pattern
+        pattern = self.uri_template.replace("{", "(?P<").replace("}", ">[^/]+)")
+        match = re.match(f"^{pattern}$", uri)
+        if match:
+            return match.groupdict()
+        return None
+
+    async def create_resource(
+        self,
+        uri: str,
+        params: dict[str, Any],
+        context: Context[ServerSessionT, LifespanContextT, RequestT] | None = None,
+    ) -> Resource:
+        """Create a resource from the template with the given parameters."""
+        try:
+            # Add context to params if needed
+            params = inject_context(self.fn, params, context, self.context_kwarg)
+
+            # Call function and check if result is a coroutine
+            result = self.fn(**params)
+            if inspect.iscoroutine(result):
+                result = await result
+
+            return FunctionResource(
+                uri=uri,  # type: ignore
+                name=self.name,
+                title=self.title,
+                description=self.description,
+                mime_type=self.mime_type,
+                icons=self.icons,
+                annotations=self.annotations,
+                fn=lambda: result,  # Capture result in closure
+            )
+        except Exception as e:
+            raise ValueError(f"Error creating resource from template: {e}")

backend_service/venv/lib/python3.13/site-packages/mcp/server/fastmcp/resources/types.py

@@ -0,0 +1,201 @@
+"""Concrete resource implementations."""
+
+import inspect
+import json
+from collections.abc import Callable
+from pathlib import Path
+from typing import Any
+
+import anyio
+import anyio.to_thread
+import httpx
+import pydantic
+import pydantic_core
+from pydantic import AnyUrl, Field, ValidationInfo, validate_call
+
+from mcp.server.fastmcp.resources.base import Resource
+from mcp.types import Annotations, Icon
+
+
+class TextResource(Resource):
+    """A resource that reads from a string."""
+
+    text: str = Field(description="Text content of the resource")
+
+    async def read(self) -> str:
+        """Read the text content."""
+        return self.text  # pragma: no cover
+
+
+class BinaryResource(Resource):
+    """A resource that reads from bytes."""
+
+    data: bytes = Field(description="Binary content of the resource")
+
+    async def read(self) -> bytes:
+        """Read the binary content."""
+        return self.data  # pragma: no cover
+
+
+class FunctionResource(Resource):
+    """A resource that defers data loading by wrapping a function.
+
+    The function is only called when the resource is read, allowing for lazy loading
+    of potentially expensive data. This is particularly useful when listing resources,
+    as the function won't be called until the resource is actually accessed.
+
+    The function can return:
+    - str for text content (default)
+    - bytes for binary content
+    - other types will be converted to JSON
+    """
+
+    fn: Callable[[], Any] = Field(exclude=True)
+
+    async def read(self) -> str | bytes:
+        """Read the resource by calling the wrapped function."""
+        try:
+            # Call the function first to see if it returns a coroutine
+            result = self.fn()
+            # If it's a coroutine, await it
+            if inspect.iscoroutine(result):
+                result = await result
+
+            if isinstance(result, Resource):  # pragma: no cover
+                return await result.read()
+            elif isinstance(result, bytes):
+                return result
+            elif isinstance(result, str):
+                return result
+            else:
+                return pydantic_core.to_json(result, fallback=str, indent=2).decode()
+        except Exception as e:
+            raise ValueError(f"Error reading resource {self.uri}: {e}")
+
+    @classmethod
+    def from_function(
+        cls,
+        fn: Callable[..., Any],
+        uri: str,
+        name: str | None = None,
+        title: str | None = None,
+        description: str | None = None,
+        mime_type: str | None = None,
+        icons: list[Icon] | None = None,
+        annotations: Annotations | None = None,
+    ) -> "FunctionResource":
+        """Create a FunctionResource from a function."""
+        func_name = name or fn.__name__
+        if func_name == "<lambda>":  # pragma: no cover
+            raise ValueError("You must provide a name for lambda functions")
+
+        # ensure the arguments are properly cast
+        fn = validate_call(fn)
+
+        return cls(
+            uri=AnyUrl(uri),
+            name=func_name,
+            title=title,
+            description=description or fn.__doc__ or "",
+            mime_type=mime_type or "text/plain",
+            fn=fn,
+            icons=icons,
+            annotations=annotations,
+        )
+
+
+class FileResource(Resource):
+    """A resource that reads from a file.
+
+    Set is_binary=True to read file as binary data instead of text.
+    """
+
+    path: Path = Field(description="Path to the file")
+    is_binary: bool = Field(
+        default=False,
+        description="Whether to read the file as binary data",
+    )
+    mime_type: str = Field(
+        default="text/plain",
+        description="MIME type of the resource content",
+    )
+
+    @pydantic.field_validator("path")
+    @classmethod
+    def validate_absolute_path(cls, path: Path) -> Path:  # pragma: no cover
+        """Ensure path is absolute."""
+        if not path.is_absolute():
+            raise ValueError("Path must be absolute")
+        return path
+
+    @pydantic.field_validator("is_binary")
+    @classmethod
+    def set_binary_from_mime_type(cls, is_binary: bool, info: ValidationInfo) -> bool:
+        """Set is_binary based on mime_type if not explicitly set."""
+        if is_binary:
+            return True
+        mime_type = info.data.get("mime_type", "text/plain")
+        return not mime_type.startswith("text/")
+
+    async def read(self) -> str | bytes:
+        """Read the file content."""
+        try:
+            if self.is_binary:
+                return await anyio.to_thread.run_sync(self.path.read_bytes)
+            return await anyio.to_thread.run_sync(self.path.read_text)
+        except Exception as e:
+            raise ValueError(f"Error reading file {self.path}: {e}")
+
+
+class HttpResource(Resource):
+    """A resource that reads from an HTTP endpoint."""
+
+    url: str = Field(description="URL to fetch content from")
+    mime_type: str = Field(default="application/json", description="MIME type of the resource content")
+
+    async def read(self) -> str | bytes:
+        """Read the HTTP content."""
+        async with httpx.AsyncClient() as client:  # pragma: no cover
+            response = await client.get(self.url)
+            response.raise_for_status()
+            return response.text
+
+
+class DirectoryResource(Resource):
+    """A resource that lists files in a directory."""
+
+    path: Path = Field(description="Path to the directory")
+    recursive: bool = Field(default=False, description="Whether to list files recursively")
+    pattern: str | None = Field(default=None, description="Optional glob pattern to filter files")
+    mime_type: str = Field(default="application/json", description="MIME type of the resource content")
+
+    @pydantic.field_validator("path")
+    @classmethod
+    def validate_absolute_path(cls, path: Path) -> Path:  # pragma: no cover
+        """Ensure path is absolute."""
+        if not path.is_absolute():
+            raise ValueError("Path must be absolute")
+        return path
+
+    def list_files(self) -> list[Path]:  # pragma: no cover
+        """List files in the directory."""
+        if not self.path.exists():
+            raise FileNotFoundError(f"Directory not found: {self.path}")
+        if not self.path.is_dir():
+            raise NotADirectoryError(f"Not a directory: {self.path}")
+
+        try:
+            if self.pattern:
+                return list(self.path.glob(self.pattern)) if not self.recursive else list(self.path.rglob(self.pattern))
+            return list(self.path.glob("*")) if not self.recursive else list(self.path.rglob("*"))
+        except Exception as e:
+            raise ValueError(f"Error listing directory {self.path}: {e}")
+
+    async def read(self) -> str:  # Always returns JSON string  # pragma: no cover
+        """Read the directory listing."""
+        try:
+            files = await anyio.to_thread.run_sync(self.list_files)
+            file_list = [str(f.relative_to(self.path)) for f in files if f.is_file()]
+            return json.dumps({"files": file_list}, indent=2)
+        except Exception as e:
+            raise ValueError(f"Error reading directory {self.path}: {e}")

backend_service/venv/lib/python3.13/site-packages/mcp/server/fastmcp/tools/__init__.py

@@ -0,0 +1,4 @@
+from .base import Tool
+from .tool_manager import ToolManager
+
+__all__ = ["Tool", "ToolManager"]

backend_service/venv/lib/python3.13/site-packages/mcp/server/fastmcp/tools/base.py

@@ -0,0 +1,118 @@
+from __future__ import annotations as _annotations
+
+import functools
+import inspect
+from collections.abc import Callable
+from functools import cached_property
+from typing import TYPE_CHECKING, Any
+
+from pydantic import BaseModel, Field
+
+from mcp.server.fastmcp.exceptions import ToolError
+from mcp.server.fastmcp.utilities.context_injection import find_context_parameter
+from mcp.server.fastmcp.utilities.func_metadata import FuncMetadata, func_metadata
+from mcp.types import Icon, ToolAnnotations
+
+if TYPE_CHECKING:
+    from mcp.server.fastmcp.server import Context
+    from mcp.server.session import ServerSessionT
+    from mcp.shared.context import LifespanContextT, RequestT
+
+
+class Tool(BaseModel):
+    """Internal tool registration info."""
+
+    fn: Callable[..., Any] = Field(exclude=True)
+    name: str = Field(description="Name of the tool")
+    title: str | None = Field(None, description="Human-readable title of the tool")
+    description: str = Field(description="Description of what the tool does")
+    parameters: dict[str, Any] = Field(description="JSON schema for tool parameters")
+    fn_metadata: FuncMetadata = Field(
+        description="Metadata about the function including a pydantic model for tool arguments"
+    )
+    is_async: bool = Field(description="Whether the tool is async")
+    context_kwarg: str | None = Field(None, description="Name of the kwarg that should receive context")
+    annotations: ToolAnnotations | None = Field(None, description="Optional annotations for the tool")
+    icons: list[Icon] | None = Field(default=None, description="Optional list of icons for this tool")
+    meta: dict[str, Any] | None = Field(default=None, description="Optional metadata for this tool")
+
+    @cached_property
+    def output_schema(self) -> dict[str, Any] | None:
+        return self.fn_metadata.output_schema
+
+    @classmethod
+    def from_function(
+        cls,
+        fn: Callable[..., Any],
+        name: str | None = None,
+        title: str | None = None,
+        description: str | None = None,
+        context_kwarg: str | None = None,
+        annotations: ToolAnnotations | None = None,
+        icons: list[Icon] | None = None,
+        meta: dict[str, Any] | None = None,
+        structured_output: bool | None = None,
+    ) -> Tool:
+        """Create a Tool from a function."""
+        func_name = name or fn.__name__
+
+        if func_name == "<lambda>":
+            raise ValueError("You must provide a name for lambda functions")
+
+        func_doc = description or fn.__doc__ or ""
+        is_async = _is_async_callable(fn)
+
+        if context_kwarg is None:  # pragma: no branch
+            context_kwarg = find_context_parameter(fn)
+
+        func_arg_metadata = func_metadata(
+            fn,
+            skip_names=[context_kwarg] if context_kwarg is not None else [],
+            structured_output=structured_output,
+        )
+        parameters = func_arg_metadata.arg_model.model_json_schema(by_alias=True)
+
+        return cls(
+            fn=fn,
+            name=func_name,
+            title=title,
+            description=func_doc,
+            parameters=parameters,
+            fn_metadata=func_arg_metadata,
+            is_async=is_async,
+            context_kwarg=context_kwarg,
+            annotations=annotations,
+            icons=icons,
+            meta=meta,
+        )
+
+    async def run(
+        self,
+        arguments: dict[str, Any],
+        context: Context[ServerSessionT, LifespanContextT, RequestT] | None = None,
+        convert_result: bool = False,
+    ) -> Any:
+        """Run the tool with arguments."""
+        try:
+            result = await self.fn_metadata.call_fn_with_arg_validation(
+                self.fn,
+                self.is_async,
+                arguments,
+                {self.context_kwarg: context} if self.context_kwarg is not None else None,
+            )
+
+            if convert_result:
+                result = self.fn_metadata.convert_result(result)
+
+            return result
+        except Exception as e:
+            raise ToolError(f"Error executing tool {self.name}: {e}") from e
+
+
+def _is_async_callable(obj: Any) -> bool:
+    while isinstance(obj, functools.partial):  # pragma: no cover
+        obj = obj.func
+
+    return inspect.iscoroutinefunction(obj) or (
+        callable(obj) and inspect.iscoroutinefunction(getattr(obj, "__call__", None))
+    )

backend_service/venv/lib/python3.13/site-packages/mcp/server/fastmcp/tools/tool_manager.py

@@ -0,0 +1,93 @@
+from __future__ import annotations as _annotations
+
+from collections.abc import Callable
+from typing import TYPE_CHECKING, Any
+
+from mcp.server.fastmcp.exceptions import ToolError
+from mcp.server.fastmcp.tools.base import Tool
+from mcp.server.fastmcp.utilities.logging import get_logger
+from mcp.shared.context import LifespanContextT, RequestT
+from mcp.types import Icon, ToolAnnotations
+
+if TYPE_CHECKING:
+    from mcp.server.fastmcp.server import Context
+    from mcp.server.session import ServerSessionT
+
+logger = get_logger(__name__)
+
+
+class ToolManager:
+    """Manages FastMCP tools."""
+
+    def __init__(
+        self,
+        warn_on_duplicate_tools: bool = True,
+        *,
+        tools: list[Tool] | None = None,
+    ):
+        self._tools: dict[str, Tool] = {}
+        if tools is not None:
+            for tool in tools:
+                if warn_on_duplicate_tools and tool.name in self._tools:
+                    logger.warning(f"Tool already exists: {tool.name}")
+                self._tools[tool.name] = tool
+
+        self.warn_on_duplicate_tools = warn_on_duplicate_tools
+
+    def get_tool(self, name: str) -> Tool | None:
+        """Get tool by name."""
+        return self._tools.get(name)
+
+    def list_tools(self) -> list[Tool]:
+        """List all registered tools."""
+        return list(self._tools.values())
+
+    def add_tool(
+        self,
+        fn: Callable[..., Any],
+        name: str | None = None,
+        title: str | None = None,
+        description: str | None = None,
+        annotations: ToolAnnotations | None = None,
+        icons: list[Icon] | None = None,
+        meta: dict[str, Any] | None = None,
+        structured_output: bool | None = None,
+    ) -> Tool:
+        """Add a tool to the server."""
+        tool = Tool.from_function(
+            fn,
+            name=name,
+            title=title,
+            description=description,
+            annotations=annotations,
+            icons=icons,
+            meta=meta,
+            structured_output=structured_output,
+        )
+        existing = self._tools.get(tool.name)
+        if existing:
+            if self.warn_on_duplicate_tools:
+                logger.warning(f"Tool already exists: {tool.name}")
+            return existing
+        self._tools[tool.name] = tool
+        return tool
+
+    def remove_tool(self, name: str) -> None:
+        """Remove a tool by name."""
+        if name not in self._tools:
+            raise ToolError(f"Unknown tool: {name}")
+        del self._tools[name]
+
+    async def call_tool(
+        self,
+        name: str,
+        arguments: dict[str, Any],
+        context: Context[ServerSessionT, LifespanContextT, RequestT] | None = None,
+        convert_result: bool = False,
+    ) -> Any:
+        """Call a tool by name with arguments."""
+        tool = self.get_tool(name)
+        if not tool:
+            raise ToolError(f"Unknown tool: {name}")
+
+        return await tool.run(arguments, context=context, convert_result=convert_result)

backend_service/venv/lib/python3.13/site-packages/mcp/server/fastmcp/utilities/__init__.py

@@ -0,0 +1 @@
+"""FastMCP utility modules."""

backend_service/venv/lib/python3.13/site-packages/mcp/server/fastmcp/utilities/context_injection.py

@@ -0,0 +1,68 @@
+"""Context injection utilities for FastMCP."""
+
+from __future__ import annotations
+
+import inspect
+import typing
+from collections.abc import Callable
+from typing import Any
+
+
+def find_context_parameter(fn: Callable[..., Any]) -> str | None:
+    """Find the parameter that should receive the Context object.
+
+    Searches through the function's signature to find a parameter
+    with a Context type annotation.
+
+    Args:
+        fn: The function to inspect
+
+    Returns:
+        The name of the context parameter, or None if not found
+    """
+    from mcp.server.fastmcp.server import Context
+
+    # Get type hints to properly resolve string annotations
+    try:
+        hints = typing.get_type_hints(fn)
+    except Exception:
+        # If we can't resolve type hints, we can't find the context parameter
+        return None
+
+    # Check each parameter's type hint
+    for param_name, annotation in hints.items():
+        # Handle direct Context type
+        if inspect.isclass(annotation) and issubclass(annotation, Context):
+            return param_name
+
+        # Handle generic types like Optional[Context]
+        origin = typing.get_origin(annotation)
+        if origin is not None:
+            args = typing.get_args(annotation)
+            for arg in args:
+                if inspect.isclass(arg) and issubclass(arg, Context):
+                    return param_name
+
+    return None
+
+
+def inject_context(
+    fn: Callable[..., Any],
+    kwargs: dict[str, Any],
+    context: Any | None,
+    context_kwarg: str | None,
+) -> dict[str, Any]:
+    """Inject context into function kwargs if needed.
+
+    Args:
+        fn: The function that will be called
+        kwargs: The current keyword arguments
+        context: The context object to inject (if any)
+        context_kwarg: The name of the parameter to inject into
+
+    Returns:
+        Updated kwargs with context injected if applicable
+    """
+    if context_kwarg is not None and context is not None:
+        return {**kwargs, context_kwarg: context}
+    return kwargs

backend_service/venv/lib/python3.13/site-packages/mcp/server/fastmcp/utilities/func_metadata.py

@@ -0,0 +1,533 @@
+import inspect
+import json
+from collections.abc import Awaitable, Callable, Sequence
+from itertools import chain
+from types import GenericAlias
+from typing import Annotated, Any, cast, get_args, get_origin, get_type_hints
+
+import pydantic_core
+from pydantic import (
+    BaseModel,
+    ConfigDict,
+    Field,
+    RootModel,
+    WithJsonSchema,
+    create_model,
+)
+from pydantic.fields import FieldInfo
+from pydantic.json_schema import GenerateJsonSchema, JsonSchemaWarningKind
+from typing_extensions import is_typeddict
+from typing_inspection.introspection import (
+    UNKNOWN,
+    AnnotationSource,
+    ForbiddenQualifier,
+    inspect_annotation,
+    is_union_origin,
+)
+
+from mcp.server.fastmcp.exceptions import InvalidSignature
+from mcp.server.fastmcp.utilities.logging import get_logger
+from mcp.server.fastmcp.utilities.types import Audio, Image
+from mcp.types import CallToolResult, 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.
+        """
+        if isinstance(result, CallToolResult):
+            if self.output_schema is not None:
+                assert self.output_model is not None, "Output model must be set if output schema is defined"
+                self.output_model.model_validate(result.structuredContent)
+            return result
+
+        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, data_value in data.items():
+            if data_key not in key_to_field_info:  # pragma: no cover
+                continue
+
+            field_info = key_to_field_info[data_key]
+            if isinstance(data_value, str) and field_info.annotation is not str:
+                try:
+                    pre_parsed = json.loads(data_value)
+                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, 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.
+    """
+    try:
+        sig = inspect.signature(func, eval_str=True)
+    except NameError as e:  # pragma: no cover
+        # This raise could perhaps be skipped, and we (FastMCP) just call
+        # model_rebuild right before using it 🤷
+        raise InvalidSignature(f"Unable to evaluate type annotations for callable {func.__name__!r}") from e
+    params = sig.parameters
+    dynamic_pydantic_model_params: dict[str, Any] = {}
+    for param in params.values():
+        if param.name.startswith("_"):  # pragma: no cover
+            raise InvalidSignature(f"Parameter {param.name} of {func.__name__} cannot start with '_'")
+        if param.name in skip_names:
+            continue
+
+        annotation = param.annotation if param.annotation is not inspect.Parameter.empty else Any
+        field_name = param.name
+        field_kwargs: dict[str, Any] = {}
+        field_metadata: list[Any] = []
+
+        if param.annotation is inspect.Parameter.empty:
+            field_metadata.append(WithJsonSchema({"title": param.name, "type": "string"}))
+        # Check if the parameter name conflicts with BaseModel attributes
+        # This is necessary because Pydantic warns about shadowing parent attributes
+        if hasattr(BaseModel, field_name) and callable(getattr(BaseModel, field_name)):
+            # Use an alias to avoid the shadowing warning
+            field_kwargs["alias"] = field_name
+            # Use a prefixed field name
+            field_name = f"field_{field_name}"
+
+        if param.default is not inspect.Parameter.empty:
+            dynamic_pydantic_model_params[field_name] = (
+                Annotated[(annotation, *field_metadata, Field(**field_kwargs))],
+                param.default,
+            )
+        else:
+            dynamic_pydantic_model_params[field_name] = Annotated[(annotation, *field_metadata, Field(**field_kwargs))]
+
+    arguments_model = create_model(
+        f"{func.__name__}Arguments",
+        __base__=ArgModelBase,
+        **dynamic_pydantic_model_params,
+    )
+
+    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")
+
+    try:
+        inspected_return_ann = inspect_annotation(sig.return_annotation, annotation_source=AnnotationSource.FUNCTION)
+    except ForbiddenQualifier as e:
+        raise InvalidSignature(f"Function {func.__name__}: return annotation contains an invalid type qualifier") from e
+
+    return_type_expr = inspected_return_ann.type
+
+    # `AnnotationSource.FUNCTION` allows no type qualifier to be used, so `return_type_expr` is guaranteed to *not* be
+    # unknown (i.e. a bare `Final`).
+    assert return_type_expr is not UNKNOWN
+
+    if is_union_origin(get_origin(return_type_expr)):
+        args = get_args(return_type_expr)
+        # Check if CallToolResult appears in the union (excluding None for Optional check)
+        if any(isinstance(arg, type) and issubclass(arg, CallToolResult) for arg in args if arg is not type(None)):
+            raise InvalidSignature(
+                f"Function {func.__name__}: CallToolResult cannot be used in Union or Optional types. "
+                "To return empty results, use: CallToolResult(content=[])"
+            )
+
+    original_annotation: Any
+    # if the typehint is CallToolResult, the user either intends to return without validation
+    # or they provided validation as Annotated metadata
+    if isinstance(return_type_expr, type) and issubclass(return_type_expr, CallToolResult):
+        if inspected_return_ann.metadata:
+            return_type_expr = inspected_return_ann.metadata[0]
+            if len(inspected_return_ann.metadata) >= 2:
+                # Reconstruct the original annotation, by preserving the remaining metadata,
+                # i.e. from `Annotated[CallToolResult, ReturnType, Gt(1)]` to
+                # `Annotated[ReturnType, Gt(1)]`:
+                original_annotation = Annotated[
+                    (return_type_expr, *inspected_return_ann.metadata[1:])
+                ]  # pragma: no cover
+            else:
+                # We only had `Annotated[CallToolResult, ReturnType]`, treat the original annotation
+                # as beging `ReturnType`:
+                original_annotation = return_type_expr
+        else:
+            return FuncMetadata(arg_model=arguments_model)
+    else:
+        original_annotation = sig.return_annotation
+
+    output_model, output_schema, wrap_output = _try_create_model_and_schema(
+        original_annotation, return_type_expr, func.__name__
+    )
+
+    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 {return_type_expr} 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(
+    original_annotation: Any,
+    type_expr: Any,
+    func_name: str,
+) -> tuple[type[BaseModel] | None, dict[str, Any] | None, bool]:
+    """Try to create a model and schema for the given annotation without warnings.
+
+    Args:
+        original_annotation: The original return annotation (may be wrapped in `Annotated`).
+        type_expr: The underlying type expression derived from the return annotation
+            (`Annotated` and type qualifiers were stripped).
+        func_name: The name of the function.
+
+    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 type_expr is None:
+        model = _create_wrapped_model(func_name, original_annotation)
+        wrap_output = True
+
+    # Handle GenericAlias types (list[str], dict[str, int], Union[str, int], etc.)
+    elif isinstance(type_expr, GenericAlias):
+        origin = get_origin(type_expr)
+
+        # Special case: dict with string keys can use RootModel
+        if origin is dict:
+            args = get_args(type_expr)
+            if len(args) == 2 and args[0] is str:
+                # TODO: should we use the original annotation? We are loosing any potential `Annotated`
+                # metadata for Pydantic here:
+                model = _create_dict_model(func_name, type_expr)
+            else:
+                # dict with non-str keys needs wrapping
+                model = _create_wrapped_model(func_name, original_annotation)
+                wrap_output = True
+        else:
+            # All other generic types need wrapping (list, tuple, Union, Optional, etc.)
+            model = _create_wrapped_model(func_name, original_annotation)
+            wrap_output = True
+
+    # Handle regular type objects
+    elif isinstance(type_expr, type):
+        type_annotation = cast(type[Any], type_expr)
+
+        # Case 1: BaseModel subclasses (can be used directly)
+        if issubclass(type_annotation, BaseModel):
+            model = type_annotation
+
+        # Case 2: TypedDicts:
+        elif is_typeddict(type_annotation):
+            model = _create_model_from_typeddict(type_annotation)
+
+        # Case 3: Primitive types that need wrapping
+        elif type_annotation in (str, int, float, bool, bytes, type(None)):
+            model = _create_wrapped_model(func_name, original_annotation)
+            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, type_hints)
+            # 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, original_annotation)
+        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 {type_expr} in {func_name}: {type(e).__name__}: {e}")
+            return None, None, False
+
+        return model, schema, wrap_output
+
+    return None, None, False
+
+
+_no_default = object()
+
+
+def _create_model_from_class(cls: type[Any], type_hints: dict[str, 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., `type_hints` is non-empty)
+    """
+    model_fields: dict[str, Any] = {}
+    for field_name, field_type in type_hints.items():
+        if field_name.startswith("_"):  # pragma: no cover
+            continue
+
+        default = getattr(cls, field_name, _no_default)
+        if default is _no_default:
+            model_fields[field_name] = field_type
+        else:
+            model_fields[field_name] = (field_type, default)
+
+    return create_model(cls.__name__, __config__=ConfigDict(from_attributes=True), **model_fields)
+
+
+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():
+        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
+            model_fields[field_name] = (field_type, None)
+        else:
+            model_fields[field_name] = field_type
+
+    return create_model(td_type.__name__, **model_fields)
+
+
+def _create_wrapped_model(func_name: str, annotation: Any) -> 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"
+
+    return create_model(model_name, result=annotation)
+
+
+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 _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:  # pragma: no cover
+        return []
+
+    if isinstance(result, ContentBlock):
+        return [result]
+
+    if isinstance(result, Image):
+        return [result.to_image_content()]
+
+    if isinstance(result, Audio):
+        return [result.to_audio_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)]

backend_service/venv/lib/python3.13/site-packages/mcp/server/fastmcp/utilities/logging.py

@@ -0,0 +1,43 @@
+"""Logging utilities for FastMCP."""
+
+import logging
+from typing import Literal
+
+
+def get_logger(name: str) -> logging.Logger:
+    """Get a logger nested under MCPnamespace.
+
+    Args:
+        name: the name of the logger, which will be prefixed with 'FastMCP.'
+
+    Returns:
+        a configured logger instance
+    """
+    return logging.getLogger(name)
+
+
+def configure_logging(
+    level: Literal["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"] = "INFO",
+) -> None:
+    """Configure logging for MCP.
+
+    Args:
+        level: the log level to use
+    """
+    handlers: list[logging.Handler] = []
+    try:  # pragma: no cover
+        from rich.console import Console
+        from rich.logging import RichHandler
+
+        handlers.append(RichHandler(console=Console(stderr=True), rich_tracebacks=True))
+    except ImportError:  # pragma: no cover
+        pass
+
+    if not handlers:  # pragma: no cover
+        handlers.append(logging.StreamHandler())
+
+    logging.basicConfig(
+        level=level,
+        format="%(message)s",
+        handlers=handlers,
+    )

backend_service/venv/lib/python3.13/site-packages/mcp/server/fastmcp/utilities/types.py

@@ -0,0 +1,101 @@
+"""Common types used across FastMCP."""
+
+import base64
+from pathlib import Path
+
+from mcp.types import AudioContent, ImageContent
+
+
+class Image:
+    """Helper class for returning images from tools."""
+
+    def __init__(
+        self,
+        path: str | Path | None = None,
+        data: bytes | None = None,
+        format: str | None = None,
+    ):
+        if path is None and data is None:  # pragma: no cover
+            raise ValueError("Either path or data must be provided")
+        if path is not None and data is not None:  # pragma: no cover
+            raise ValueError("Only one of path or data can be provided")
+
+        self.path = Path(path) if path else None
+        self.data = data
+        self._format = format
+        self._mime_type = self._get_mime_type()
+
+    def _get_mime_type(self) -> str:
+        """Get MIME type from format or guess from file extension."""
+        if self._format:  # pragma: no cover
+            return f"image/{self._format.lower()}"
+
+        if self.path:
+            suffix = self.path.suffix.lower()
+            return {
+                ".png": "image/png",
+                ".jpg": "image/jpeg",
+                ".jpeg": "image/jpeg",
+                ".gif": "image/gif",
+                ".webp": "image/webp",
+            }.get(suffix, "application/octet-stream")
+        return "image/png"  # pragma: no cover  # default for raw binary data
+
+    def to_image_content(self) -> ImageContent:
+        """Convert to MCP ImageContent."""
+        if self.path:
+            with open(self.path, "rb") as f:
+                data = base64.b64encode(f.read()).decode()
+        elif self.data is not None:  # pragma: no cover
+            data = base64.b64encode(self.data).decode()
+        else:  # pragma: no cover
+            raise ValueError("No image data available")
+
+        return ImageContent(type="image", data=data, mimeType=self._mime_type)
+
+
+class Audio:
+    """Helper class for returning audio from tools."""
+
+    def __init__(
+        self,
+        path: str | Path | None = None,
+        data: bytes | None = None,
+        format: str | None = None,
+    ):
+        if not bool(path) ^ bool(data):  # pragma: no cover
+            raise ValueError("Either path or data can be provided")
+
+        self.path = Path(path) if path else None
+        self.data = data
+        self._format = format
+        self._mime_type = self._get_mime_type()
+
+    def _get_mime_type(self) -> str:
+        """Get MIME type from format or guess from file extension."""
+        if self._format:  # pragma: no cover
+            return f"audio/{self._format.lower()}"
+
+        if self.path:
+            suffix = self.path.suffix.lower()
+            return {
+                ".wav": "audio/wav",
+                ".mp3": "audio/mpeg",
+                ".ogg": "audio/ogg",
+                ".flac": "audio/flac",
+                ".aac": "audio/aac",
+                ".m4a": "audio/mp4",
+            }.get(suffix, "application/octet-stream")
+        return "audio/wav"  # pragma: no cover  # default for raw binary data
+
+    def to_audio_content(self) -> AudioContent:
+        """Convert to MCP AudioContent."""
+        if self.path:
+            with open(self.path, "rb") as f:
+                data = base64.b64encode(f.read()).decode()
+        elif self.data is not None:  # pragma: no cover
+            data = base64.b64encode(self.data).decode()
+        else:  # pragma: no cover
+            raise ValueError("No audio data available")
+
+        return AudioContent(type="audio", data=data, mimeType=self._mime_type)