Documentation Index
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Use this file to discover all available pages before exploring further.
Overview
Bedrock Chat implements agents using the ReAct (Reasoning + Acting) approach, powered by Strands Agents, an open-source SDK for building AI agents.
You can develop custom tools to extend the capabilities of your agents using Python decorators and the Strands framework.
Strands provides a simple @tool decorator that transforms regular Python functions into AI agent tools. The decorator automatically extracts information from your function’s docstring and type hints to create tool specifications that the LLM can understand and use.
For detailed information, see the Python Tools documentation.
Create a function decorated with @tool from Strands:
from strands import tool
@tool
def calculator(expression: str) -> dict:
"""
Perform mathematical calculations safely.
Args:
expression: Mathematical expression to evaluate (e.g., "2+2", "10*5", "sqrt(16)")
Returns:
dict: Result in Strands format with toolUseId, status, and content
"""
try:
# Your calculation logic here
result = eval(expression) # Note: Use safe evaluation in production
return {
"toolUseId": "placeholder",
"status": "success",
"content": [{"text": str(result)}]
}
except Exception as e:
return {
"toolUseId": "placeholder",
"status": "error",
"content": [{"text": f"Error: {str(e)}"}]
}
Tools with Bot Context (Closure Pattern)
To access bot information (BotModel), use a closure pattern that captures the bot context:
from strands import tool
from app.repositories.models.custom_bot import BotModel
def create_calculator_tool(bot: BotModel | None = None):
"""Create calculator tool with bot context closure."""
@tool
def calculator(expression: str) -> dict:
"""
Perform mathematical calculations safely.
Args:
expression: Mathematical expression to evaluate (e.g., "2+2", "10*5", "sqrt(16)")
Returns:
dict: Result in Strands format with toolUseId, status, and content
"""
# Access bot context within the tool
if bot:
print(f"Tool used by bot: {bot.id}")
try:
result = eval(expression) # Use safe evaluation in production
return {
"toolUseId": "placeholder",
"status": "success",
"content": [{"text": str(result)}]
}
except Exception as e:
return {
"toolUseId": "placeholder",
"status": "error",
"content": [{"text": f"Error: {str(e)}"}]
}
return calculator
Complete Example: BMI Calculator
Here’s a complete example demonstrating best practices:
from strands import tool
from app.repositories.models.custom_bot import BotModel
def create_bmi_tool(bot: BotModel | None = None):
"""Create BMI calculation tool with bot context closure."""
@tool
def calculate_bmi(height: float, weight: float) -> dict:
"""
Calculate the Body Mass Index (BMI) from height and weight.
Args:
height: Height in centimeters (cm). e.g. 170.0
weight: Weight in kilograms (kg). e.g. 70.0
Returns:
dict: BMI calculation result in Strands format
"""
# Access bot context if needed
if bot:
print(f"BMI calculation for bot: {bot.id}")
try:
if height <= 0 or weight <= 0:
return {
"toolUseId": "placeholder",
"status": "error",
"content": [{"text": "Error: Height and weight must be positive numbers."}]
}
height_in_meters = height / 100
bmi = weight / (height_in_meters**2)
bmi_rounded = round(bmi, 1)
if bmi < 18.5:
category = "Underweight"
elif bmi < 25:
category = "Normal weight"
elif bmi < 30:
category = "Overweight"
else:
category = "Obese"
result_data = {
"bmi": bmi_rounded,
"category": category,
"height_cm": height,
"weight_kg": weight
}
return {
"toolUseId": "placeholder",
"status": "success",
"content": [{"json": result_data}]
}
except Exception as e:
return {
"toolUseId": "placeholder",
"status": "error",
"content": [{"text": f"BMI calculation error: {str(e)}"}]
}
return calculate_bmi
{
"toolUseId": "placeholder", # Will be replaced by Strands
"status": "success" | "error",
"content": [
{"text": "Simple text response"} |
{"json": {"key": "Complex data object"}}
]
}
- Use
{"text": "message"} for simple text responses
- Use
{"json": data} for complex data that should be preserved as structured information
- Always set
status to either "success" or "error"
Implementation Guidelines
Documentation
The function name and docstring are used when the LLM considers which tool to use. The docstring is embedded in the prompt, so describe the tool’s purpose and parameters precisely.
File Location
Place your implementation file in the backend/app/strands_integration/tools/ directory.
Registration
Open backend/app/strands_integration/utils.py and edit get_strands_registered_tools to include your new tool.
Frontend Labels (Optional)
Add clear names and descriptions for the frontend UI. While optional, this improves UX by providing user-friendly explanations instead of using the technical function names.
- Edit
frontend/src/i18n/en/index.ts and add your name and description under agent.tools
- Edit other language files (e.g.,
ja/index.ts) as needed
Deployment
Run npx cdk deploy to deploy your changes and make your custom tool available in the custom bot screen.
Once deployed, users can enable your custom tool in the Agent section of the custom bot screen by toggling the switch next to the tool name. When enabled, the agent can utilize the tool when processing user queries.