Toolbox

Description

Toolboxes extend the Davinci Agent’s capabilities by providing custom Python functions that can connect to external APIs, perform specialized operations, and retrieve real-time data. When you enable a toolbox, its tools become available to the agent, allowing it to call them when needed during conversations and task execution. Toolboxes are ideal for integrating third-party services (weather, databases, web APIs), automating data retrieval, performing specialized calculations, and extending agent functionality beyond built-in capabilities.

Creating a Toolbox

Toolboxes are Python code objects that define one or more tools using the davinci.tool() decorator. Each tool is an asynchronous function that the agent can call with specific parameters.

Basic Structure

A typical toolbox contains:

Imports: Required libraries (httpx for API calls, typing for type hints)
Constants: API endpoints, configuration values
Helper Functions: Internal functions that support tool operations
Tool Functions: Functions marked with davinci.tool() that the agent calls
Documentation: Docstrings that explain what each tool does

Example skeleton:

from typing import Any
import httpx

# Constants
API_BASE = "https://api.example.com"

# Helper function (not exposed to agent)
async def make_request(url: str) -> dict:
    async with httpx.AsyncClient() as client:
        response = await client.get(url)
        return response.json()

# Tool function (exposed to agent)
davinci.tool()
async def get_data(parameter: str) -> str:
    """Tool description goes here.
    
    Args:
        parameter: Description of this parameter
    """
    data = await make_request(f"{API_BASE}/{parameter}")
    return format_result(data)

Defining Tools

The davinci.tool() Decorator

Place davinci.tool() immediately before any async function you want to expose to the agent:

davinci.tool()
async def my_tool_name(param1: str, param2: float) -> str:
    """Tool description."""
    # Implementation here
    return result

Key points:

Must be an async function
Must have a docstring (agent uses this to understand the tool)
Parameters should have type hints
Return type should be specified

Function Documentation

The docstring is critical—the agent reads it to understand when and how to use your tool. Docstring format:

davinci.tool()
async def get_weather(city: str, units: str = "metric") -> str:
    """Get current weather for a city.
    
    Args:
        city: City name (e.g., "San Francisco", "London")
        units: Temperature units - "metric" (Celsius) or "imperial" (Fahrenheit)
    """

Docstring sections:

Description: First line(s) explain what the tool does
Args: List each parameter with its purpose and format
Return (optional): Describe what the tool returns

Write clear, concise docstrings. The agent uses them to decide when to call your tool and what arguments to provide.

Connecting to APIs

Toolboxes commonly use the httpx library for making HTTP requests to external APIs.

Making API Requests

Use httpx.AsyncClient() for asynchronous API calls:

async def fetch_from_api(endpoint: str) -> dict:
    """Make an async GET request to an API."""
    async with httpx.AsyncClient() as client:
        response = await client.get(
            endpoint,
            headers={"User-Agent": "MyApp/1.0"},
            timeout=30.0
        )
        response.raise_for_status()  # Raise exception for 4xx/5xx
        return response.json()

Best practices:

Always use async with for proper connection cleanup
Set appropriate timeouts (default: 30 seconds)
Include a User-Agent header (some APIs require it)
Use raise_for_status() to catch HTTP errors
Handle exceptions gracefully

Error Handling

Wrap API calls in try-except blocks to handle failures:

davinci.tool()
async def get_data(query: str) -> str:
    """Fetch data from API."""
    try:
        async with httpx.AsyncClient() as client:
            response = await client.get(f"{API_BASE}/{query}")
            response.raise_for_status()
            data = response.json()
            return format_result(data)
    except httpx.HTTPError:
        return "Unable to fetch data from API."
    except Exception:
        return "An unexpected error occurred."

Always handle exceptions. If a tool crashes, it disrupts agent execution. Return error messages as strings instead of raising exceptions.

Enabling and Using Toolboxes

Activating a Toolbox

To make a toolbox available to the agent:

Open the toolbox in the Toolbox view
Toggle the Active/Inactive switch in the header to “Active”
The toolbox’s tools are now available to the agent

Only active toolboxes are available to the agent. Deactivate toolboxes you’re not using to reduce agent complexity.

How the Agent Uses Tools

When you interact with the agent, it:

Analyzes your request: Determines if any tools can help
Selects appropriate tools: Chooses tools based on docstrings
Calls tools with parameters: Invokes tools with extracted arguments
Uses results: Incorporates tool output into its response

Example conversation:

User: "What's the weather in California?"

Agent thinking: User wants weather data. I have a get_alerts tool 
that works with state codes. California = CA.

Agent: *Calls get_alerts(state="CA")*
Agent: "Here are the current weather alerts for California: ..."

The agent automatically determines when to use your tools based on the conversation context and tool descriptions.

Updating Model Data from Tools

Tools can write data back to your model using the same update() function as regular code:

davinci.tool()
async def fetch_and_store_temperature(city: str) -> str:
    """Fetch temperature and store it in the model.
    
    Args:
        city: City name to get temperature for
    """
    # Fetch from API
    temp = await get_temperature_from_api(city)
    
    # Update model attribute
    update(@TemperatureAttribute, temp, "celsius", "number")
    
    return f"Stored temperature: {temp}°C"

This allows tools to both retrieve external data and populate your model automatically.

Testing Toolboxes

Always test your toolbox before using it with the agent.

Using the Test Interface

The right pane of the Toolbox view provides a testing area:

Type a natural language instruction (e.g., “Get weather alerts for Texas”)
Click Run Test
Review the output to verify your tool works correctly

Test different scenarios:

Valid inputs
Edge cases (empty strings, unusual values)
Error conditions (invalid API keys, network failures)
Different parameter combinations

Thorough testing prevents agent failures during actual use. Test each tool with various inputs before activating the toolbox.

Example: Weather Data Toolbox

The default weather toolbox provides real-time weather data from the National Weather Service API. This example demonstrates API integration, error handling, and creating multiple related tools within one toolbox.

Complete Script

Here’s the full example weather toolbox. We will break it down below to explain each section.

from typing import Any
import httpx

# Constants
NWS_API_BASE = "https://api.weather.gov"
USER_AGENT = "weather-app/1.0"

async def make_nws_request(url: str) -> dict[str, Any] | None:
    """Make a request to the NWS API with proper error handling."""
    headers = {
        "User-Agent": USER_AGENT,
        "Accept": "application/geo+json"
    }
    
    async with httpx.AsyncClient() as client:
        try:
            response = await client.get(url, headers=headers, timeout=30.0)
            response.raise_for_status()
            return response.json()
        except Exception:
            return None

def format_alert(feature: dict) -> str:
    """Format an alert feature into a readable string."""
    props = feature["properties"]
    return f"""
Event: {props.get('event', 'Unknown')}
Area: {props.get('areaDesc', 'Unknown')}
Severity: {props.get('severity', 'Unknown')}
Description: {props.get('description', 'No description available')}
Instructions: {props.get('instruction', 'No specific instructions provided')}
"""

davinci.tool()
async def get_alerts(state: str) -> str:
    """Get weather alerts for a US state.

    Args:
        state: Two-letter US state code (e.g. CA, NY)
    """
    url = f"{NWS_API_BASE}/alerts/active/area/{state}"
    data = await make_nws_request(url)

    if not data or "features" not in data:
        return "Unable to fetch alerts or no alerts found."

    if not data["features"]:
        return "No active alerts for this state."

    alerts = [format_alert(feature) for feature in data["features"]]
    return "\n---\n".join(alerts)

davinci.tool()
async def get_forecast(latitude: float, longitude: float) -> str:
    """Get weather forecast for a location.

    Args:
        latitude: Latitude of the location
        longitude: Longitude of the location
    """
    # First get the forecast grid endpoint
    points_url = f"{NWS_API_BASE}/points/{latitude},{longitude}"
    points_data = await make_nws_request(points_url)

    if not points_data:
        return "Unable to fetch forecast data for this location."

    # Get the forecast URL from the points response
    forecast_url = points_data["properties"]["forecast"]
    forecast_data = await make_nws_request(forecast_url)

    if not forecast_data:
        return "Unable to fetch detailed forecast."

    # Format the periods into a readable forecast
    periods = forecast_data["properties"]["periods"]
    forecasts = []
    for period in periods[:5]:  # Only show next 5 periods
        forecast = f"""
{period['name']}:
Temperature: {period['temperature']}°{period['temperatureUnit']}
Wind: {period['windSpeed']} {period['windDirection']}
Forecast: {period['detailedForecast']}
"""
        forecasts.append(forecast)

    return "\n---\n".join(forecasts)

Imports and Constants

from typing import Any
import httpx

# Constants
NWS_API_BASE = "https://api.weather.gov"
USER_AGENT = "weather-app/1.0"

Lines 1-2: Imports

typing.Any: Type hint for dictionaries with unknown structure
httpx: Async HTTP client library for API requests

Lines 4-5: Constants

NWS_API_BASE: Base URL for National Weather Service API
USER_AGENT: Identifier for API requests (NWS requires this)

Helper Function: make_nws_request

async def make_nws_request(url: str) -> dict[str, Any] | None:
    """Make a request to the NWS API with proper error handling."""
    headers = {
        "User-Agent": USER_AGENT,
        "Accept": "application/geo+json"
    }

Line 7: Helper function definition

async def: Asynchronous function (doesn’t block while waiting for API)
url: str: Takes a URL string as input
-> dict[str, Any] | None: Returns either a dictionary or None if it fails

Lines 8-11: Request headers

User-Agent: Required by NWS API to identify the client
Accept: Tells API we want GeoJSON format response

    async with httpx.AsyncClient() as client:
        try:
            response = await client.get(url, headers=headers, timeout=30.0)
            response.raise_for_status()
            return response.json()
        except Exception:
            return None

Line 13: Create async HTTP client

async with: Ensures connection closes properly after use
httpx.AsyncClient(): Creates reusable HTTP client

Lines 14-18: Make request and handle response

await client.get(): Make GET request, wait for response
timeout=30.0: Wait up to 30 seconds before giving up
raise_for_status(): Throw error if response is 4xx or 5xx
response.json(): Parse JSON response into Python dictionary

Lines 19-20: Error handling

Catch any exception (network error, timeout, invalid JSON)
Return None to indicate failure (caller checks for this)

Helper Function: format_alert

def format_alert(feature: dict) -> str:
    """Format an alert feature into a readable string."""
    props = feature["properties"]
    return f"""
Event: {props.get('event', 'Unknown')}
Area: {props.get('areaDesc', 'Unknown')}
Severity: {props.get('severity', 'Unknown')}
Description: {props.get('description', 'No description available')}
Instructions: {props.get('instruction', 'No specific instructions provided')}
"""

Line 22: Helper function for formatting

Takes raw API data (feature) and makes it human-readable

Line 24: Extract properties

feature["properties"]: Get the properties section from API response

Lines 25-30: Format as string

Uses f-string with multi-line format
.get() method provides default values if fields are missing
Returns formatted string with event details

Tool 1: get_alerts

davinci.tool()
async def get_alerts(state: str) -> str:
    """Get weather alerts for a US state.

    Args:
        state: Two-letter US state code (e.g. CA, NY)
    """

Line 32: Tool decorator

davinci.tool(): Marks this function as available to the agent

Line 33: Tool function definition

async def: Async function (can make API calls without blocking)
get_alerts: Function name (agent sees this as tool name)
state: str: Required parameter—a string state code
-> str: Returns a string (formatted alert information)

Lines 34-38: Docstring

First line: Clear description of what the tool does
Args: section: Explains the state parameter
Agent reads this to understand when/how to use the tool

    url = f"{NWS_API_BASE}/alerts/active/area/{state}"
    data = await make_nws_request(url)

Line 39: Build API URL

Uses f-string to insert state code into URL
Example: For “CA”, creates https://api.weather.gov/alerts/active/area/CA

Line 40: Make API request

Calls helper function to fetch data
await: Wait for request to complete before continuing
data will be dictionary (success) or None (failure)

    if not data or "features" not in data:
        return "Unable to fetch alerts or no alerts found."

    if not data["features"]:
        return "No active alerts for this state."

Lines 42-43: Check for API failure

If data is None or doesn’t have “features” key, API failed
Return error message to agent

Lines 45-46: Check for empty alerts

If “features” exists but is empty, no alerts are active
Return informative message instead of empty response

    alerts = [format_alert(feature) for feature in data["features"]]
    return "\n---\n".join(alerts)

Line 48: Format all alerts

List comprehension: Process each alert in the data
format_alert(feature): Convert each raw alert to readable text
Creates list of formatted alert strings

Line 49: Join and return

"\n---\n".join(): Combine all alerts with separator lines
Returns single string with all formatted alerts
Agent receives this string and can present it to the user

Tool 2: get_forecast

davinci.tool()
async def get_forecast(latitude: float, longitude: float) -> str:
    """Get weather forecast for a location.

    Args:
        latitude: Latitude of the location
        longitude: Longitude of the location
    """
    # First get the forecast grid endpoint
    points_url = f"{NWS_API_BASE}/points/{latitude},{longitude}"
    points_data = await make_nws_request(points_url)

    if not points_data:
        return "Unable to fetch forecast data for this location."

    # Get the forecast URL from the points response
    forecast_url = points_data["properties"]["forecast"]
    forecast_data = await make_nws_request(forecast_url)

    if not forecast_data:
        return "Unable to fetch detailed forecast."

    # Format the periods into a readable forecast
    periods = forecast_data["properties"]["periods"]
    forecasts = []
    for period in periods[:5]:  # Only show next 5 periods
        forecast = f"""
{period['name']}:
Temperature: {period['temperature']}°{period['temperatureUnit']}
Wind: {period['windSpeed']} {period['windDirection']}
Forecast: {period['detailedForecast']}
"""
        forecasts.append(forecast)

    return "\n---\n".join(forecasts)

This tool demonstrates:

Multi-step API calls (first get grid point, then get forecast)
Handling latitude/longitude coordinates
Processing nested API responses
Limiting output (only 5 forecast periods)
Formatting complex data structures

The NWS API requires two steps: first call /points/ to get grid coordinates, then call the forecast URL from that response. The tool handles this complexity and returns simple formatted text.

Tips for Effective Toolboxes

Keep Tools Focused: Each tool should do one thing well. Multiple simple tools are better than one complex tool. Write Clear Docstrings: The agent’s ability to use your tools depends entirely on your descriptions. Be specific and include examples. Handle Errors Gracefully: Return error messages instead of crashing. The agent can communicate errors to users. Use Type Hints: They improve code clarity and help catch bugs during development. Test Thoroughly: Test with various inputs, including edge cases and error conditions. Limit Output Length: Don’t return megabytes of data. Summarize, truncate, or format data before returning it. Add Timeouts: Network requests can hang. Always specify reasonable timeouts.

View Types

View	Description
Toolbox	Edit tool code, view detected tools, and test functionality.
Properties	Edit the object’s properties, attributes, and metadata.

Properties Fields

Name

string

Name of the object.

Short Name

string

Short name of the object.

Documentation

string

Description of the object.

Code

string

The Python code that defines the toolbox tools and connections.

Relationships

connection

A list of all Relationships this object has with other model objects.Read more about Relationships

Model-Based Systems Engineering

Project Management

Artifacts

Simulation & Behavior

Data & Tools

Description

Creating a Toolbox

Basic Structure

Defining Tools

The davinci.tool() Decorator

Function Documentation

Connecting to APIs

Making API Requests

Error Handling

Enabling and Using Toolboxes

Activating a Toolbox

How the Agent Uses Tools

Updating Model Data from Tools

Testing Toolboxes

Using the Test Interface

Example: Weather Data Toolbox

Complete Script

Imports and Constants

Helper Function: make_nws_request

Helper Function: format_alert

Tool 1: get_alerts

Tool 2: get_forecast

Tips for Effective Toolboxes

View Types

Properties Fields

Model-Based Systems Engineering

Project Management

Artifacts

Simulation & Behavior

Data & Tools

​Description

​Creating a Toolbox

​Basic Structure

​Defining Tools

​The davinci.tool() Decorator

​Function Documentation

​Connecting to APIs

​Making API Requests

​Error Handling

​Enabling and Using Toolboxes

​Activating a Toolbox

​How the Agent Uses Tools

​Updating Model Data from Tools

​Testing Toolboxes

​Using the Test Interface

​Example: Weather Data Toolbox

​Complete Script

​Imports and Constants

​Helper Function: make_nws_request

​Helper Function: format_alert

​Tool 1: get_alerts

​Tool 2: get_forecast

​Tips for Effective Toolboxes

​View Types

​Properties Fields

Description

Creating a Toolbox

Basic Structure

Defining Tools

The davinci.tool() Decorator

Function Documentation

Connecting to APIs

Making API Requests

Error Handling

Enabling and Using Toolboxes

Activating a Toolbox

How the Agent Uses Tools

Updating Model Data from Tools

Testing Toolboxes

Using the Test Interface

Example: Weather Data Toolbox

Complete Script

Imports and Constants

Helper Function: make_nws_request

Helper Function: format_alert

Tool 1: get_alerts

Tool 2: get_forecast

Tips for Effective Toolboxes

View Types

Properties Fields