feat: expose tool_use and agent through ToolContext to decorated tools

src/strands/__init__.py

@@ -3,5 +3,6 @@
 from . import agent, models, telemetry, types
 from .agent.agent import Agent
 from .tools.decorator import tool
+from .types.tools import ToolContext
 
-__all__ = ["Agent", "agent", "models", "tool", "types", "telemetry"]
+__all__ = ["Agent", "agent", "models", "tool", "types", "telemetry", "ToolContext"]

src/strands/tools/decorator.py

@@ -61,7 +61,7 @@ def my_tool(param1: str, param2: int = 42) -> dict:
 from pydantic import BaseModel, Field, create_model
 from typing_extensions import override
 
-from ..types.tools import AgentTool, JSONSchema, ToolGenerator, ToolSpec, ToolUse
+from ..types.tools import AgentTool, JSONSchema, ToolContext, ToolGenerator, ToolSpec, ToolUse
 
 logger = logging.getLogger(__name__)
 
@@ -84,16 +84,18 @@ class FunctionToolMetadata:
     validate tool usage.
     """
 
-    def __init__(self, func: Callable[..., Any]) -> None:
+    def __init__(self, func: Callable[..., Any], context_param: str | None = None) -> None:
         """Initialize with the function to process.
 
         Args:
             func: The function to extract metadata from.
                  Can be a standalone function or a class method.
+            context_param: Name of the context parameter to inject, if any.
         """
         self.func = func
         self.signature = inspect.signature(func)
         self.type_hints = get_type_hints(func)
+        self._context_param = context_param
 
         # Parse the docstring with docstring_parser
         doc_str = inspect.getdoc(func) or ""
@@ -113,16 +115,16 @@ def _create_input_model(self) -> Type[BaseModel]:
         This method analyzes the function's signature, type hints, and docstring to create a Pydantic model that can
         validate input data before passing it to the function.
 
-        Special parameters like 'self', 'cls', and 'agent' are excluded from the model.
+        Special parameters that can be automatically injected are excluded from the model.
 
         Returns:
             A Pydantic BaseModel class customized for the function's parameters.
         """
         field_definitions: dict[str, Any] = {}
 
         for name, param in self.signature.parameters.items():
-            # Skip special parameters
-            if name in ("self", "cls", "agent"):
+            # Skip parameters that will be automatically injected
+            if self._is_special_parameter(name):
                 continue
 
             # Get parameter type and default
@@ -252,6 +254,49 @@ def validate_input(self, input_data: dict[str, Any]) -> dict[str, Any]:
             error_msg = str(e)
             raise ValueError(f"Validation failed for input parameters: {error_msg}") from e
 
+    def inject_special_parameters(
+        self, validated_input: dict[str, Any], tool_use: ToolUse, invocation_state: dict[str, Any]
+    ) -> None:
+        """Inject special framework-provided parameters into the validated input.
+
+        This method automatically provides framework-level context to tools that request it
+        through their function signature.
+
+        Args:
+            validated_input: The validated input parameters (modified in place).
+            tool_use: The tool use request containing tool invocation details.
+            invocation_state: Context for the tool invocation, including agent state.
+        """
+        if self._context_param and self._context_param in self.signature.parameters:
+            tool_context = ToolContext(tool_use=tool_use, agent=invocation_state["agent"])
+            validated_input[self._context_param] = tool_context
+
+        # Inject agent if requested (backward compatibility)
+        if "agent" in self.signature.parameters and "agent" in invocation_state:
+            validated_input["agent"] = invocation_state["agent"]
+
+    def _is_special_parameter(self, param_name: str) -> bool:
+        """Check if a parameter should be automatically injected by the framework or is a standard Python method param.
+
+        Special parameters include:
+        - Standard Python method parameters: self, cls
+        - Framework-provided context parameters: agent, and configurable context parameter (defaults to tool_context)
+
+        Args:
+            param_name: The name of the parameter to check.
+
+        Returns:
+            True if the parameter should be excluded from input validation and
+            handled specially during tool execution.
+        """
+        special_params = {"self", "cls", "agent"}
+
+        # Add context parameter if configured
+        if self._context_param:
+            special_params.add(self._context_param)
+
+        return param_name in special_params
+
 
 P = ParamSpec("P")  # Captures all parameters
 R = TypeVar("R")  # Return type
@@ -402,9 +447,8 @@ async def stream(self, tool_use: ToolUse, invocation_state: dict[str, Any], **kw
             # Validate input against the Pydantic model
             validated_input = self._metadata.validate_input(tool_input)
 
-            # Pass along the agent if provided and expected by the function
-            if "agent" in invocation_state and "agent" in self._metadata.signature.parameters:
-                validated_input["agent"] = invocation_state.get("agent")
+            # Inject special framework-provided parameters
+            self._metadata.inject_special_parameters(validated_input, tool_use, invocation_state)
 
             # "Too few arguments" expected, hence the type ignore
             if inspect.iscoroutinefunction(self._tool_func):
@@ -474,6 +518,7 @@ def tool(
     description: Optional[str] = None,
     inputSchema: Optional[JSONSchema] = None,
     name: Optional[str] = None,
+    context: bool | str = False,
 ) -> Callable[[Callable[P, R]], DecoratedFunctionTool[P, R]]: ...
 # Suppressing the type error because we want callers to be able to use both `tool` and `tool()` at the
 # call site, but the actual implementation handles that and it's not representable via the type-system
@@ -482,6 +527,7 @@ def tool(  # type: ignore
     description: Optional[str] = None,
     inputSchema: Optional[JSONSchema] = None,
     name: Optional[str] = None,
+    context: bool | str = False,
 ) -> Union[DecoratedFunctionTool[P, R], Callable[[Callable[P, R]], DecoratedFunctionTool[P, R]]]:
     """Decorator that transforms a Python function into a Strands tool.
 
@@ -507,6 +553,9 @@ def tool(  # type: ignore
         description: Optional custom description to override the function's docstring.
         inputSchema: Optional custom JSON schema to override the automatically generated schema.
         name: Optional custom name to override the function's name.
+        context: When provided, places a  object in the designated parameter. If True, the param name
+            defaults to 'tool_context', or if an override is needed, set context equal to a string to designate
+            the param name.
 
     Returns:
         An AgentTool that also mimics the original function when invoked
@@ -536,15 +585,24 @@ def my_tool(name: str, count: int = 1) -> str:
 
     Example with parameters:
         ```python
-        @tool(name="custom_tool", description="A tool with a custom name and description")
-        def my_tool(name: str, count: int = 1) -> str:
-            return f"Processed {name} {count} times"
+        @tool(name="custom_tool", description="A tool with a custom name and description", context=True)
+        def my_tool(name: str, count: int = 1, tool_context: ) -> str:
+            tool_id = tool_context["tool_use"]["toolUseId"]
+            return f"Processed {name} {count} times with tool ID {tool_id}"
         ```
     """
 
     def decorator(f: T) -> "DecoratedFunctionTool[P, R]":
+        # Resolve context parameter name
+        if isinstance(context, bool):
+            context_param = "tool_context" if context else None
+        else:
+            context_param = context.strip()
+            if not context_param:
+                raise ValueError("Context parameter name cannot be empty")
+
         # Create function tool metadata
-        tool_meta = FunctionToolMetadata(f)
+        tool_meta = FunctionToolMetadata(f, context_param)
         tool_spec = tool_meta.extract_metadata()
         if name is not None:
             tool_spec["name"] = name

src/strands/types/tools.py

@@ -6,12 +6,16 @@
 """
 
 from abc import ABC, abstractmethod
-from typing import Any, AsyncGenerator, Awaitable, Callable, Literal, Protocol, Union
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, Any, AsyncGenerator, Awaitable, Callable, Literal, Protocol, Union
 
 from typing_extensions import TypedDict
 
 from .media import DocumentContent, ImageContent
 
+if TYPE_CHECKING:
+    from .. import Agent
+
 JSONSchema = dict
 """Type alias for JSON Schema dictionaries."""
 
@@ -117,6 +121,23 @@ class ToolChoiceTool(TypedDict):
     name: str
 
 
+@dataclass
+class ToolContext:
+    """Context object containing framework-provided data for decorated tools.
+
+    This object provides access to framework-level information that may be useful
+    for tool implementations.
+
+    Attributes:
+        tool_use: The complete ToolUse object containing tool invocation details.
+        agent: The Agent instance executing this tool, providing access to conversation history,
+               model configuration, and other agent state.
+    """
+
+    tool_use: ToolUse
+    agent: "Agent"
+
+
 ToolChoice = Union[
     dict[Literal["auto"], ToolChoiceAuto],
     dict[Literal["any"], ToolChoiceAny],

tests/strands/tools/test_decorator.py

@@ -8,7 +8,8 @@
 import pytest
 
 import strands
-from strands.types.tools import ToolUse
+from strands import Agent
+from strands.types.tools import AgentTool, ToolContext, ToolUse
 
 
 @pytest.fixture(scope="module")
@@ -1036,3 +1037,48 @@ def complex_schema_tool(union_param: Union[List[int], Dict[str, Any], str, None]
     result = (await alist(stream))[-1]
     assert result["status"] == "success"
     assert "NoneType: None" in result["content"][0]["text"]
+
+
+@pytest.mark.asyncio
+async def test_tool_context_injection(alist):
+    """Test that ToolContext is properly injected into tools that request it."""
+
+    @strands.tool(context=True)
+    def context_tool(message: str, agent: Agent, tool_context: ToolContext) -> dict:
+        """Tool that uses ToolContext to access tool_use_id."""
+        tool_use_id = tool_context.tool_use["toolUseId"]
+        tool_name = tool_context.tool_use["name"]
+        agent_from_tool_context = tool_context.agent
+
+        return {
+            "status": "success",
+            "content": [
+                {"text": f"Tool '{tool_name}' (ID: {tool_use_id})"},
+                {"text": f"injected agent '{agent.name}' processed: {message}"},
+                {"text": f"context agent '{agent_from_tool_context.name}'"},
+            ],
+        }
+
+    tool: AgentTool = context_tool
+    generator = tool.stream(
+        tool_use={
+            "toolUseId": "test-id",
+            "name": "context_tool",
+            "input": {
+                "message": "some_message"  # note that we do not include agent nor tool context
+            },
+        },
+        invocation_state={
+            "agent": Agent(name="test_agent"),
+        },
+    )
+    tool_results = [value async for value in generator]
+
+    assert len(tool_results) == 1
+    tool_result = tool_results[0]
+
+    assert tool_result["status"] == "success"
+    assert tool_result["toolUseId"] == "test-id"
+    assert tool_result["content"][0]["text"] == "Tool 'context_tool' (ID: test-id)"
+    assert tool_result["content"][1]["text"] == "injected agent 'test_agent' processed: some_message"
+    assert tool_result["content"][2]["text"] == "context agent 'test_agent'"

tests_integ/test_tool_context_injection.py

@@ -0,0 +1,56 @@
+#!/usr/bin/env python3
+"""
+Integration test for ToolContext functionality with real agent interactions.
+"""
+
+from strands import Agent, ToolContext, tool
+from strands.types.tools import ToolResult
+
+
+@tool(context="custom_context_field")
+def good_story(message: str, custom_context_field: ToolContext) -> dict:
+    """Tool that writes a good story"""
+    tool_use_id = custom_context_field.tool_use["toolUseId"]
+    return {
+        "status": "success",
+        "content": [{"text": f"Context tool processed with ID: {tool_use_id}"}],
+    }
+
+
+@tool(context=True)
+def bad_story(message: str, tool_context: ToolContext) -> dict:
+    """Tool that writes a bad story"""
+    tool_use_id = tool_context.tool_use["toolUseId"]
+    return {
+        "status": "success",
+        "content": [{"text": f"Context tool processed with ID: {tool_use_id}"}],
+    }
+
+
+def _validate_tool_result_content(agent: Agent):
+    first_tool_result: ToolResult = [
+        block["toolResult"] for message in agent.messages for block in message["content"] if "toolResult" in block
+    ][0]
+
+    assert first_tool_result["status"] == "success"
+    assert (
+        first_tool_result["content"][0]["text"] == f"Context tool processed with ID: {first_tool_result['toolUseId']}"
+    )
+
+
+def test_strands_context_integration_context_true():
+    """Test ToolContext functionality with real agent interactions."""
+
+    agent = Agent(tools=[good_story])
+    agent("using a tool, write a good story")
+
+    _validate_tool_result_content(agent)
+
+
+def test_strands_context_integration_context_custom():
+    """Test ToolContext functionality with real agent interactions."""
+
+    agent = Agent(tools=[bad_story])
+    agent("using a tool, write a bad story")
+
+    _validate_tool_result_content(agent)