Overview
The Tool Selection evaluator determines whether an LLM selected the most appropriate tool (or tools) for a given task. This evaluator focuses on the what of tool calling - validating that the right tool was chosen - rather than whether the invocation arguments were correct.
When to Use
Use the Tool Selection evaluator when you need to:
- Validate tool choice decisions - Ensure the LLM picks the most appropriate tool for the task
- Detect hallucinated tools - Identify when the LLM tries to use tools that don’t exist
- Evaluate tool necessity - Check if the LLM correctly determines when tools are (or aren’t) needed
- Assess multi-tool selection - Validate when the LLM needs to select multiple tools for complex tasks
This evaluator validates tool selection correctness, not invocation correctness. For evaluating whether tool arguments are properly formatted, use the Tool Invocation evaluator instead. Supported Levels
The level of an evaluator determines the scope of the evaluation in OpenTelemetry terms. Some evaluations are applicable to individual spans, some to full traces or sessions, and some are applicable at multiple levels.
| Level | Supported | Notes |
|---|
| Span | Yes | Best for LLM spans that contain tool calls. Evaluate individual tool selection decisions. |
| Field | Type | Description |
|---|
input | string | The conversation context or user query |
available_tools | string | List of available tools and their descriptions |
tool_selection | string | The tool(s) selected by the LLM |
In TypeScript, the fields use camelCase: availableTools and toolSelection.
input (conversation context adapted from input messages):
User: I need to book a flight from New York to Los Angeles
Assistant: I'd be happy to help you book a flight. When would you like to travel?
User: Tomorrow morning, the earliest available
available_tools (tool descriptions adapted by JSON schemas):
book_flight: Book a flight between two cities. Requires origin, destination, and date.
search_hotels: Search for hotel accommodations by city and dates.
get_weather: Get current weather conditions for a location.
cancel_booking: Cancel an existing flight or hotel reservation.
Tool argument descriptions are optional; the focus is on the selection itself so tool names and descriptions are sufficient.
tool_selection (the LLM’s tool selection adapted from tool_calls in the output):
If the LLM did not produce any tool calls, you can put “No tools called” as the tool_selection input.
Output Interpretation
The evaluator returns a Score object with the following properties:
| Property | Value | Description |
|---|
label | "correct" or "incorrect" | Classification result |
score | 1.0 or 0.0 | Numeric score (1.0 = correct, 0.0 = incorrect) |
explanation | string | LLM-generated reasoning for the classification |
direction | "maximize" | Higher scores are better |
metadata | object | Additional information such as the model name. When tracing is enabled, includes the trace_id for the evaluation. |
- The LLM chose the best available tool for the user query
- The tool name exists in the available tools list
- The tool selection is safe and appropriate
- The correct number of tools were selected for the task
Criteria for Incorrect (0.0):
- The LLM used a hallucinated or nonexistent tool
- The LLM selected a tool when none was needed
- The LLM did not use a tool when one was required
- The LLM chose a suboptimal or irrelevant tool
Usage Examples
from phoenix.evals import LLM
from phoenix.evals.metrics import ToolSelectionEvaluator
# Initialize the LLM client
llm = LLM(provider="openai", model="gpt-4o")
# Create the evaluator
tool_selection_eval = ToolSelectionEvaluator(llm=llm)
# Inspect the evaluator's requirements
print(tool_selection_eval.describe())
# Evaluate a tool selection using human-readable format
eval_input = {
"input": """User: I need to book a flight from New York to Los Angeles
Assistant: I'd be happy to help you book a flight. When would you like to travel?
User: Tomorrow morning, the earliest available""",
"available_tools": """book_flight: Book a flight between two cities. Requires origin, destination, and date.
search_hotels: Search for hotel accommodations by city and dates.
get_weather: Get current weather conditions for a location.
cancel_booking: Cancel an existing flight or hotel reservation.""",
"tool_selection": "book_flight"
}
scores = tool_selection_eval.evaluate(eval_input)
print(scores[0])
# Score(name='tool_selection', score=1.0, label='correct', ...)
import { createToolSelectionEvaluator } from "@arizeai/phoenix-evals";
import { openai } from "@ai-sdk/openai";
// Create the evaluator
const toolSelectionEvaluator = createToolSelectionEvaluator({
model: openai("gpt-4o"),
});
// Evaluate a tool selection using human-readable format
const result = await toolSelectionEvaluator.evaluate({
input: `User: I need to book a flight from New York to Los Angeles
Assistant: I'd be happy to help you book a flight. When would you like to travel?
User: Tomorrow morning, the earliest available`,
availableTools: `book_flight: Book a flight between two cities. Requires origin, destination, and date.
search_hotels: Search for hotel accommodations by city and dates.
get_weather: Get current weather conditions for a location.
cancel_booking: Cancel an existing flight or hotel reservation.`,
toolSelection: "book_flight",
});
console.log(result);
// { score: 1, label: "correct", explanation: "..." }
from phoenix.evals import LLM
from phoenix.evals.metrics import ToolSelectionEvaluator
llm = LLM(provider="openai", model="gpt-4o")
tool_selection_eval = ToolSelectionEvaluator(llm=llm)
eval_input = {
"conversation": """User: I want to search for flights to Paris
Assistant: Sure, I can help with that. When are you planning to travel?
User: Next weekend""",
"tools_available": """flight_search: Search for available flights by destination and date.
hotel_search: Search for hotel accommodations.
car_rental: Search for rental car options.""",
"selected_tool": "flight_search"
}
input_mapping = {
"input": "conversation",
"available_tools": "tools_available",
"tool_selection": "selected_tool"
}
scores = tool_selection_eval.evaluate(eval_input, input_mapping)
import { bindEvaluator, createToolSelectionEvaluator } from "@arizeai/phoenix-evals";
import { openai } from "@ai-sdk/openai";
const toolSelectionEvaluator = createToolSelectionEvaluator({
model: openai("gpt-4o"),
});
const boundEvaluator = bindEvaluator(toolSelectionEvaluator, {
inputMapping: {
input: "conversation",
availableTools: "toolsAvailable",
toolSelection: "selectedTool",
},
});
const result = await boundEvaluator.evaluate({
conversation: `User: I want to search for flights to Paris
Assistant: Sure, I can help with that. When are you planning to travel?
User: Next weekend`,
toolsAvailable: `flight_search: Search for available flights by destination and date.
hotel_search: Search for hotel accommodations.
car_rental: Search for rental car options.`,
selectedTool: "flight_search",
});
Configuration
For LLM client configuration options, see Configuring the LLM.
Viewing and Modifying the Prompt
You can view the latest versions of our prompt templates on GitHub. The evaluators are designed to work well in a variety of contexts, but we highly recommend modifying the prompt to be more specific to your use case. Feel free to adapt them.
from phoenix.evals.metrics import ToolSelectionEvaluator
from phoenix.evals import LLM, ClassificationEvaluator
llm = LLM(provider="openai", model="gpt-4o")
evaluator = ToolSelectionEvaluator(llm=llm)
# View the prompt template
print(evaluator.prompt_template)
# Create a custom evaluator based on the built-in template
custom_evaluator = ClassificationEvaluator(
name="tool_selection",
prompt_template=evaluator.prompt_template, # Modify as needed
llm=llm,
choices={"correct": 1.0, "incorrect": 0.0},
direction="maximize",
)
import { TOOL_SELECTION_CLASSIFICATION_EVALUATOR_CONFIG, createToolSelectionEvaluator } from "@arizeai/phoenix-evals";
import { openai } from "@ai-sdk/openai";
// View the prompt template
console.log(TOOL_SELECTION_CLASSIFICATION_EVALUATOR_CONFIG.template);
// Create a custom evaluator with a modified template
const customEvaluator = createToolSelectionEvaluator({
model: openai("gpt-4o"),
promptTemplate: TOOL_SELECTION_CLASSIFICATION_EVALUATOR_CONFIG.template, // Modify as needed
});
Using with Phoenix
Evaluating Traces
Run evaluations on traces collected in Phoenix and log results as annotations:
Running Experiments
Use the Tool Selection evaluator in Phoenix experiments:
API Reference
