feat: Added an Fast API new endpoint to serve eval metric info

src/google/adk/cli/adk_web_server.py

@@ -64,6 +64,7 @@
 from ..evaluation.eval_metrics import EvalMetric
 from ..evaluation.eval_metrics import EvalMetricResult
 from ..evaluation.eval_metrics import EvalMetricResultPerInvocation
+from ..evaluation.eval_metrics import MetricInfo
 from ..evaluation.eval_result import EvalSetResult
 from ..evaluation.eval_set_results_manager import EvalSetResultsManager
 from ..evaluation.eval_sets_manager import EvalSetsManager
@@ -697,6 +698,24 @@ def list_eval_results(app_name: str) -> list[str]:
       """Lists all eval results for the given app."""
       return self.eval_set_results_manager.list_eval_set_results(app_name)
 
+    @app.get(
+        "/apps/{app_name}/eval_metrics",
+        response_model_exclude_none=True,
+    )
+    def list_eval_metrics(app_name: str) -> list[MetricInfo]:
+      """Lists all eval metrics for the given app."""
+      try:
+        from ..evaluation.metric_evaluator_registry import DEFAULT_METRIC_EVALUATOR_REGISTRY
+
+        # Right now we ignore the app_name as eval metrics are not tied to the
+        # app_name, but they could be moving forward.
+        return DEFAULT_METRIC_EVALUATOR_REGISTRY.get_registered_metrics()
+      except ModuleNotFoundError as e:
+        logger.exception("%s\n%s", MISSING_EVAL_DEPENDENCIES_MESSAGE, e)
+        raise HTTPException(
+            status_code=400, detail=MISSING_EVAL_DEPENDENCIES_MESSAGE
+        ) from e
+
     @app.delete("/apps/{app_name}/users/{user_id}/sessions/{session_id}")
     async def delete_session(app_name: str, user_id: str, session_id: str):
       await self.session_service.delete_session(

src/google/adk/evaluation/eval_metrics.py

@@ -49,16 +49,22 @@ class JudgeModelOptions(BaseModel):
 
   judge_model: str = Field(
       default="gemini-2.5-flash",
-      description="""The judge model to use for evaluation. It can be a model name.""",
+      description=(
+          "The judge model to use for evaluation. It can be a model name."
+      ),
   )
 
   judge_model_config: Optional[genai_types.GenerateContentConfig] = Field(
-      default=None, description="""The configuration for the judge model."""
+      default=None,
+      description="The configuration for the judge model.",
   )
 
   num_samples: Optional[int] = Field(
       default=None,
-      description="""The number of times to sample the model for each invocation evaluation.""",
+      description=(
+          "The number of times to sample the model for each invocation"
+          " evaluation."
+      ),
   )
 
 
@@ -70,15 +76,20 @@ class EvalMetric(BaseModel):
       populate_by_name=True,
   )
 
-  metric_name: str
-  """The name of the metric."""
+  metric_name: str = Field(
+      description="The name of the metric.",
+  )
 
-  threshold: float
-  """A threshold value. Each metric decides how to interpret this threshold."""
+  threshold: float = Field(
+      description=(
+          "A threshold value. Each metric decides how to interpret this"
+          " threshold."
+      ),
+  )
 
   judge_model_options: Optional[JudgeModelOptions] = Field(
       default=None,
-      description="""Options for the judge model.""",
+      description="Options for the judge model.",
   )
 
 
@@ -90,8 +101,14 @@ class EvalMetricResult(EvalMetric):
       populate_by_name=True,
   )
 
-  score: Optional[float] = None
-  eval_status: EvalStatus
+  score: Optional[float] = Field(
+      default=None,
+      description=(
+          "Score obtained after evaluating the metric. Optional, as evaluation"
+          " might not have happened."
+      ),
+  )
+  eval_status: EvalStatus = Field(description="The status of this evaluation.")
 
 
 class EvalMetricResultPerInvocation(BaseModel):
@@ -102,11 +119,71 @@ class EvalMetricResultPerInvocation(BaseModel):
       populate_by_name=True,
   )
 
-  actual_invocation: Invocation
-  """The actual invocation, usually obtained by inferencing the agent."""
+  actual_invocation: Invocation = Field(
+      description=(
+          "The actual invocation, usually obtained by inferencing the agent."
+      )
+  )
+
+  expected_invocation: Invocation = Field(
+      description=(
+          "The expected invocation, usually the reference or golden invocation."
+      )
+  )
 
-  expected_invocation: Invocation
-  """The expected invocation, usually the reference or golden invocation."""
+  eval_metric_results: list[EvalMetricResult] = Field(
+      default=[],
+      description="Eval resutls for each applicable metric.",
+  )
+
+
+class Interval(BaseModel):
+  """Represents a range of numeric values, e.g. [0 ,1] or (2,3) or [-1, 6)."""
+
+  min_value: float = Field(description="The smaller end of the interval.")
+
+  open_at_min: bool = Field(
+      default=False,
+      description=(
+          "The interval is Open on the min end. The default value is False,"
+          " which means that we assume that the interval is Closed."
+      ),
+  )
+
+  max_value: float = Field(description="The larger end of the interval.")
+
+  open_at_max: bool = Field(
+      default=False,
+      description=(
+          "The interval is Open on the max end. The default value is False,"
+          " which means that we assume that the interval is Closed."
+      ),
+  )
 
-  eval_metric_results: list[EvalMetricResult] = []
-  """Eval resutls for each applicable metric."""
+
+class MetricValueInfo(BaseModel):
+  """Information about the type of metric value."""
+
+  interval: Optional[Interval] = Field(
+      default=None,
+      description="The values represented by the metric are of type interval.",
+  )
+
+
+class MetricInfo(BaseModel):
+  """Information about the metric that are used for Evals."""
+
+  model_config = ConfigDict(
+      alias_generator=alias_generators.to_camel,
+      populate_by_name=True,
+  )
+
+  metric_name: str = Field(description="The name of the metric.")
+
+  description: str = Field(
+      default=None, description="A 2 to 3 line description of the metric."
+  )
+
+  metric_value_info: MetricValueInfo = Field(
+      description="Information on the nature of values supported by the metric."
+  )

src/google/adk/evaluation/final_response_match_v1.py

@@ -22,18 +22,39 @@
 
 from .eval_case import Invocation
 from .eval_metrics import EvalMetric
+from .eval_metrics import Interval
+from .eval_metrics import MetricInfo
+from .eval_metrics import MetricValueInfo
+from .eval_metrics import PrebuiltMetrics
 from .evaluator import EvalStatus
 from .evaluator import EvaluationResult
 from .evaluator import Evaluator
 from .evaluator import PerInvocationResult
 
 
 class RougeEvaluator(Evaluator):
-  """Calculates the ROUGE-1 metric to compare responses."""
+  """Evaluates if agent's final response matches a golden/expected final response using Rouge_1 metric.
+
+  Value range for this metric is [0,1], with values closer to 1 more desirable.
+  """
 
   def __init__(self, eval_metric: EvalMetric):
     self._eval_metric = eval_metric
 
+  @staticmethod
+  def get_metric_info() -> MetricInfo:
+    return MetricInfo(
+        metric_name=PrebuiltMetrics.RESPONSE_MATCH_SCORE.value,
+        description=(
+            "This metric evaluates if the agent's final response matches a"
+            " golden/expected final response using Rouge_1 metric. Value range"
+            " for this metric is [0,1], with values closer to 1 more desirable."
+        ),
+        metric_value_info=MetricValueInfo(
+            interval=Interval(min_value=0.0, max_value=1.0)
+        ),
+    )
+
   @override
   def evaluate_invocations(
       self,

src/google/adk/evaluation/final_response_match_v2.py

@@ -24,6 +24,10 @@
 from ..utils.feature_decorator import experimental
 from .eval_case import Invocation
 from .eval_metrics import EvalMetric
+from .eval_metrics import Interval
+from .eval_metrics import MetricInfo
+from .eval_metrics import MetricValueInfo
+from .eval_metrics import PrebuiltMetrics
 from .evaluator import EvalStatus
 from .evaluator import EvaluationResult
 from .evaluator import PerInvocationResult
@@ -146,6 +150,20 @@ def __init__(
     if self._eval_metric.judge_model_options.num_samples is None:
       self._eval_metric.judge_model_options.num_samples = _DEFAULT_NUM_SAMPLES
 
+  @staticmethod
+  def get_metric_info() -> MetricInfo:
+    return MetricInfo(
+        metric_name=PrebuiltMetrics.FINAL_RESPONSE_MATCH_V2.value,
+        description=(
+            "This metric evaluates if the agent's final response matches a"
+            " golden/expected final response using LLM as a judge. Value range"
+            " for this metric is [0,1], with values closer to 1 more desirable."
+        ),
+        metric_value_info=MetricValueInfo(
+            interval=Interval(min_value=0.0, max_value=1.0)
+        ),
+    )
+
   @override
   def format_auto_rater_prompt(
       self, actual_invocation: Invocation, expected_invocation: Invocation
@@ -185,8 +203,7 @@ def aggregate_per_invocation_samples(
     tie, consider the result to be invalid.
 
     Args:
-      per_invocation_samples: Samples of per-invocation results to
-        aggregate.
+      per_invocation_samples: Samples of per-invocation results to aggregate.
 
     Returns:
       If there is a majority of valid results, return the first valid result.

src/google/adk/evaluation/metric_evaluator_registry.py

@@ -17,7 +17,9 @@
 import logging
 
 from ..errors.not_found_error import NotFoundError
+from ..utils.feature_decorator import experimental
 from .eval_metrics import EvalMetric
+from .eval_metrics import MetricInfo
 from .eval_metrics import MetricName
 from .eval_metrics import PrebuiltMetrics
 from .evaluator import Evaluator
@@ -29,10 +31,11 @@
 logger = logging.getLogger("google_adk." + __name__)
 
 
+@experimental
 class MetricEvaluatorRegistry:
   """A registry for metric Evaluators."""
 
-  _registry: dict[str, type[Evaluator]] = {}
+  _registry: dict[str, tuple[type[Evaluator], MetricInfo]] = {}
 
   def get_evaluator(self, eval_metric: EvalMetric) -> Evaluator:
     """Returns an Evaluator for the given metric.
@@ -48,15 +51,18 @@ def get_evaluator(self, eval_metric: EvalMetric) -> Evaluator:
     if eval_metric.metric_name not in self._registry:
       raise NotFoundError(f"{eval_metric.metric_name} not found in registry.")
 
-    return self._registry[eval_metric.metric_name](eval_metric=eval_metric)
+    return self._registry[eval_metric.metric_name][0](eval_metric=eval_metric)
 
   def register_evaluator(
-      self, metric_name: MetricName, evaluator: type[Evaluator]
+      self,
+      metric_info: MetricInfo,
+      evaluator: type[Evaluator],
   ):
-    """Registers an evaluator given the metric name.
+    """Registers an evaluator given the metric info.
 
     If a mapping already exist, then it is updated.
     """
+    metric_name = metric_info.metric_name
     if metric_name in self._registry:
       logger.info(
           "Updating Evaluator class for %s from %s to %s",
@@ -65,31 +71,45 @@ def register_evaluator(
           evaluator,
       )
 
-    self._registry[str(metric_name)] = evaluator
+    self._registry[str(metric_name)] = (evaluator, metric_info)
+
+  def get_registered_metrics(
+      self,
+  ) -> list[MetricInfo]:
+    """Returns a list of MetricInfo about the metrics registered so far."""
+    return [
+        evaluator_and_metric_info[1].model_copy(deep=True)
+        for _, evaluator_and_metric_info in self._registry.items()
+    ]
 
 
 def _get_default_metric_evaluator_registry() -> MetricEvaluatorRegistry:
   """Returns an instance of MetricEvaluatorRegistry with standard metrics already registered in it."""
   metric_evaluator_registry = MetricEvaluatorRegistry()
 
   metric_evaluator_registry.register_evaluator(
-      metric_name=PrebuiltMetrics.TOOL_TRAJECTORY_AVG_SCORE.value,
+      metric_info=TrajectoryEvaluator.get_metric_info(),
       evaluator=TrajectoryEvaluator,
   )
+
   metric_evaluator_registry.register_evaluator(
-      metric_name=PrebuiltMetrics.RESPONSE_EVALUATION_SCORE.value,
+      metric_info=ResponseEvaluator.get_metric_info(
+          PrebuiltMetrics.RESPONSE_EVALUATION_SCORE.value
+      ),
       evaluator=ResponseEvaluator,
   )
   metric_evaluator_registry.register_evaluator(
-      metric_name=PrebuiltMetrics.RESPONSE_MATCH_SCORE.value,
+      metric_info=ResponseEvaluator.get_metric_info(
+          PrebuiltMetrics.RESPONSE_MATCH_SCORE.value
+      ),
       evaluator=ResponseEvaluator,
   )
   metric_evaluator_registry.register_evaluator(
-      metric_name=PrebuiltMetrics.SAFETY_V1.value,
+      metric_info=SafetyEvaluatorV1.get_metric_info(),
       evaluator=SafetyEvaluatorV1,
   )
   metric_evaluator_registry.register_evaluator(
-      metric_name=PrebuiltMetrics.FINAL_RESPONSE_MATCH_V2.value,
+      metric_info=FinalResponseMatchV2Evaluator.get_metric_info(),
       evaluator=FinalResponseMatchV2Evaluator,
   )