Tool calls (semantic, per call)
Tool calls (semantic) gives each expected tool call a probability that an equivalent call was made: same tool, and argument values that mean the same thing even if keys are renamed or values reformatted. It is the judgment version of UiPath's ToolCallArgsEvaluator, one question per call in one request.
What the evaluator does
For M expected calls jeval adds M Noul questions. Score is the share matched; details list each call with its probability.
The question, verbatim
This is the question the API sends for the example below, generated from the same code path the playground and API use. Jev sees the request fields as state and returns a probability for each outcome. Nothing is generated, so there is nothing to parse.
`tool_calls` contains a call equivalent to `expected_tool_calls[0]`: same tool, and argument values that mean the same thing even if formatted or named differently.
Reach for it when
- Agents whose tool schemas drift (origin/destination vs from/to).
- Dates, amounts and enums that come back formatted differently.
- Any place exact-match tool tests produce false failures.
Not the right tool when
- Arguments must be byte-identical for a downstream system (use tool_call_args).
Watch out for
- Semantic equivalence can be too lenient for security-sensitive arguments (recipients, amounts); keep the exact evaluators for those.
What to send
score 0–1 (p of the good outcome), label “nn% yes”, confidence |p − 0.5| × 2, probabilities yes/no, passed at 0.5. Plus a details array with one row per tool call.
{
"evaluators": [
"tool_call_semantic"
],
"output": "booked",
"tool_calls": [
{
"name": "search_flights",
"args": {
"origin": "BLR",
"destination": "SFO",
"date": "Nov 12, 2025"
}
}
],
"expected_tool_calls": [
{
"name": "search_flights",
"args": {
"from": "BLR",
"to": "SFO",
"date": "2025-11-12"
}
}
]
}Matched with high probability; exact match would have failed.
Aggregate with
One result per item is a fact; a dataset of them is a metric. These are the aggregations that fit this evaluator's output shape.
Precision is the share of predicted positives that were really positive; recall is the share of real positives the evaluator caught; F1 is their harmonic mean, which is only high when both are.
Mean score averages the normalised 0–1 score across items.
- Does semantic tool-call matching replace exact matching?
- No, it complements it. Run both: exact for arguments that must be precise, semantic for the ones where format varies.