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Tool

Bases: BaseModel

Internal tool registration info.

Source code in src/mcp/server/mcpserver/tools/base.py 
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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")
    resolved_params: dict[str, Any] = Field(
        default_factory=lambda: {},
        exclude=True,
        description="Parameters filled by resolvers, mapped to (Resolve, wants_union)",
    )
    resolver_plans: dict[Hashable, Any] = Field(
        default_factory=lambda: {}, exclude=True, description="Static per-resolver parameter plans"
    )
    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__

        validate_and_warn_tool_name(func_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)

        resolved_params = find_resolved_parameters(fn)
        if resolved_params and returns_input_required(fn):
            raise InvalidSignature(
                f"Tool {func_name!r} combines Resolve(...) parameters with an InputRequiredResult "
                "return; a call has one input_required channel, so the multi-round flow is driven "
                "either by resolvers or by the tool body, not both"
            )

        skip_names = [context_kwarg] if context_kwarg is not None else []
        skip_names.extend(resolved_params)

        func_arg_metadata = func_metadata(
            fn,
            skip_names=skip_names,
            structured_output=structured_output,
        )
        parameters = func_arg_metadata.arg_model.model_json_schema(by_alias=True)

        # Match `model_dump_one_level`'s kwarg keys (alias when present, else field name)
        # so a by-name resolver param resolves to a key that exists at call time.
        tool_arg_names = {field.alias or name for name, field in func_arg_metadata.arg_model.model_fields.items()}
        resolver_plans = build_resolver_plans(resolved_params, tool_arg_names)

        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,
            resolved_params=dict(resolved_params),
            resolver_plans=resolver_plans,
            annotations=annotations,
            icons=icons,
            meta=meta,
        )

    async def run(
        self,
        arguments: dict[str, Any],
        context: Context[LifespanContextT, RequestT],
        convert_result: bool = False,
    ) -> Any:
        """Run the tool with arguments.

        Raises:
            ToolError: If the tool function raises during execution.
        """
        try:
            pass_directly: dict[str, Any] = {}
            if self.context_kwarg is not None:
                pass_directly[self.context_kwarg] = context

            # Resolvers see the same validated arguments the tool body receives:
            # validate once and reuse it, so a `default_factory`/stateful validator
            # can't hand a by-name resolver a different value than the body.
            pre_validated: dict[str, Any] | None = None
            if self.resolved_params:
                pre_validated = self.fn_metadata.validate_arguments(arguments)
                resolved = await resolve_arguments(self.resolved_params, self.resolver_plans, pre_validated, context)
                if isinstance(resolved, InputRequiredResult):
                    # A resolver still needs client input (>= 2026-07-28): surface the
                    # batched questions instead of running the tool body this round.
                    return self.fn_metadata.convert_result(resolved) if convert_result else resolved
                pass_directly |= resolved

            result = await self.fn_metadata.call_fn_with_arg_validation(
                self.fn,
                self.is_async,
                arguments,
                pass_directly or None,
                pre_validated=pre_validated,
            )

            # Registration rejects the annotated form of this combination; this covers
            # a body that returns an InputRequiredResult without declaring it.
            if self.resolved_params and isinstance(result, InputRequiredResult):
                raise ToolError(
                    "the tool returned an InputRequiredResult but its parameters use Resolve(...); "
                    "a call has one input_required channel, so the multi-round flow is driven "
                    "either by resolvers or by the tool body, not both"
                )

            if convert_result:
                result = self.fn_metadata.convert_result(result)

            return result
        except MCPError:
            # `MCPError` (and subclasses such as `UrlElicitationRequiredError`)
            # carries a JSON-RPC `ErrorData(code, message, data)` and means
            # "respond with a protocol error" - re-raise so the kernel surfaces
            # it as a top-level JSON-RPC error rather than wrapping it as a
            # `CallToolResult(isError=True)` execution failure.
            raise
        except Exception as e:
            raise ToolError(f"Error executing tool {self.name}: {e}") from e

from_function classmethod 

from_function(
    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.

Source code in src/mcp/server/mcpserver/tools/base.py 
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@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__

    validate_and_warn_tool_name(func_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)

    resolved_params = find_resolved_parameters(fn)
    if resolved_params and returns_input_required(fn):
        raise InvalidSignature(
            f"Tool {func_name!r} combines Resolve(...) parameters with an InputRequiredResult "
            "return; a call has one input_required channel, so the multi-round flow is driven "
            "either by resolvers or by the tool body, not both"
        )

    skip_names = [context_kwarg] if context_kwarg is not None else []
    skip_names.extend(resolved_params)

    func_arg_metadata = func_metadata(
        fn,
        skip_names=skip_names,
        structured_output=structured_output,
    )
    parameters = func_arg_metadata.arg_model.model_json_schema(by_alias=True)

    # Match `model_dump_one_level`'s kwarg keys (alias when present, else field name)
    # so a by-name resolver param resolves to a key that exists at call time.
    tool_arg_names = {field.alias or name for name, field in func_arg_metadata.arg_model.model_fields.items()}
    resolver_plans = build_resolver_plans(resolved_params, tool_arg_names)

    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,
        resolved_params=dict(resolved_params),
        resolver_plans=resolver_plans,
        annotations=annotations,
        icons=icons,
        meta=meta,
    )

run async 

run(
    arguments: dict[str, Any],
    context: Context[LifespanContextT, RequestT],
    convert_result: bool = False,
) -> Any

Run the tool with arguments.

Raises:

Type Description
ToolError

If the tool function raises during execution.
Source code in src/mcp/server/mcpserver/tools/base.py 
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async def run(
    self,
    arguments: dict[str, Any],
    context: Context[LifespanContextT, RequestT],
    convert_result: bool = False,
) -> Any:
    """Run the tool with arguments.

    Raises:
        ToolError: If the tool function raises during execution.
    """
    try:
        pass_directly: dict[str, Any] = {}
        if self.context_kwarg is not None:
            pass_directly[self.context_kwarg] = context

        # Resolvers see the same validated arguments the tool body receives:
        # validate once and reuse it, so a `default_factory`/stateful validator
        # can't hand a by-name resolver a different value than the body.
        pre_validated: dict[str, Any] | None = None
        if self.resolved_params:
            pre_validated = self.fn_metadata.validate_arguments(arguments)
            resolved = await resolve_arguments(self.resolved_params, self.resolver_plans, pre_validated, context)
            if isinstance(resolved, InputRequiredResult):
                # A resolver still needs client input (>= 2026-07-28): surface the
                # batched questions instead of running the tool body this round.
                return self.fn_metadata.convert_result(resolved) if convert_result else resolved
            pass_directly |= resolved

        result = await self.fn_metadata.call_fn_with_arg_validation(
            self.fn,
            self.is_async,
            arguments,
            pass_directly or None,
            pre_validated=pre_validated,
        )

        # Registration rejects the annotated form of this combination; this covers
        # a body that returns an InputRequiredResult without declaring it.
        if self.resolved_params and isinstance(result, InputRequiredResult):
            raise ToolError(
                "the tool returned an InputRequiredResult but its parameters use Resolve(...); "
                "a call has one input_required channel, so the multi-round flow is driven "
                "either by resolvers or by the tool body, not both"
            )

        if convert_result:
            result = self.fn_metadata.convert_result(result)

        return result
    except MCPError:
        # `MCPError` (and subclasses such as `UrlElicitationRequiredError`)
        # carries a JSON-RPC `ErrorData(code, message, data)` and means
        # "respond with a protocol error" - re-raise so the kernel surfaces
        # it as a top-level JSON-RPC error rather than wrapping it as a
        # `CallToolResult(isError=True)` execution failure.
        raise
    except Exception as e:
        raise ToolError(f"Error executing tool {self.name}: {e}") from e

ToolManager

Manages MCPServer tools.

Source code in src/mcp/server/mcpserver/tools/tool_manager.py 
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class ToolManager:
    """Manages MCPServer tools."""

    def __init__(self, warn_on_duplicate_tools: bool = True, *, tools: list[Tool] | None = None):
        self._tools: dict[str, Tool] = {}
        for tool in tools or ():
            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[LifespanContextT, RequestT],
        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, convert_result=convert_result)

get_tool 

get_tool(name: str) -> Tool | None

Get tool by name.

Source code in src/mcp/server/mcpserver/tools/tool_manager.py 
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def get_tool(self, name: str) -> Tool | None:
    """Get tool by name."""
    return self._tools.get(name)

list_tools 

list_tools() -> list[Tool]

List all registered tools.

Source code in src/mcp/server/mcpserver/tools/tool_manager.py 
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def list_tools(self) -> list[Tool]:
    """List all registered tools."""
    return list(self._tools.values())

add_tool 

add_tool(
    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.

Source code in src/mcp/server/mcpserver/tools/tool_manager.py 
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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

remove_tool 

remove_tool(name: str) -> None

Remove a tool by name.

Source code in src/mcp/server/mcpserver/tools/tool_manager.py 
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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]

call_tool async 

call_tool(
    name: str,
    arguments: dict[str, Any],
    context: Context[LifespanContextT, RequestT],
    convert_result: bool = False,
) -> Any

Call a tool by name with arguments.

Source code in src/mcp/server/mcpserver/tools/tool_manager.py 
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async def call_tool(
    self,
    name: str,
    arguments: dict[str, Any],
    context: Context[LifespanContextT, RequestT],
    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, convert_result=convert_result)