2. Tools
Explicitly define & isolate interfaces that models will call
An eight-factor AI application treats tools as clearly defined interfaces through which models can affect the outside world. A tool is any function, API, or capability that extends the model’s abilities beyond pure text generation. The collection of these interfaces forms the application’s tool registry.
Every tool in an eight-factor app must have an explicit interface declaration:
- If tool implementations are directly exposed to models, it’s not an eight-factor app – it creates tight coupling and security risks.
- Tools shared between multiple agents should be defined once and registered consistently across applications.
A tool interface consists of several key elements:
- Name and description that models can understand
- Clear input/output specifications
- Usage examples
- Permissions and constraints
- Error handling patterns
Tools should be registered with complete metadata:
tool_registry.register({
"name": "weather_lookup",
"description": "Get current weather for a location",
"parameters": {
"location": {
"type": "string",
"description": "City name or coordinates"
}
},
"returns": {
"type": "object",
"properties": {
"temperature": "number",
"conditions": "string"
}
},
"examples": [
{
"input": {"location": "New York"},
"output": {"temperature": 72, "conditions": "sunny"}
}
]
})
Bad practice - direct implementation exposure:
def get_weather(location):
# Model can directly call APIs and handle raw data
api_key = os.getenv('WEATHER_API_KEY')
response = requests.get(f'https://api.weather.com/{location}?key={api_key}')
return response.json()
Good practice - tool interface abstraction:
class WeatherTool(BaseTool):
def execute(self, location: str) -> WeatherInfo:
"""Get weather information for a location."""
try:
weather_service = self.get_weather_service()
raw_data = weather_service.fetch(location)
return WeatherInfo.from_raw(raw_data)
except WeatherServiceError as e:
raise ToolError(f"Weather lookup failed: {e}")
This approach provides several critical benefits:
- Security through controlled access to underlying services
- Consistent error handling and rate limiting
- Easy replacement of tool implementations
- Clear documentation for both developers and models
- Ability to mock tools for testing
- Usage analytics and monitoring
Some applications may dynamically discover or load tools at runtime, but the interface definitions should still be explicit and version-controlled. Tools may be implemented in different ways (API calls, local functions, external services), but the interface presented to models should remain consistent.
The tool registry should be treated as a security boundary:
- All tool access should be audited
- Tool permissions should be clearly defined
- Tool usage should be monitored and rate-limited
- Tool responses should be validated before returning to models
This pattern enables:
- Safe and controlled access to external capabilities
- Easy addition of new tools without model changes
- Clear separation between tool interface and implementation
- Systematic testing and validation of tool interactions
- Comprehensive monitoring of tool usage and performance