Merge pull request #196 from An-idd/fix/provider-model-default-drift

fix(video): runway/higgsfield model defaults diverge from schema → wrong cost & silent model swap

tests/tools/test_provider_model_defaults.py

@@ -0,0 +1,44 @@
+"""Regression: a provider's code-level model default must match its schema's
+declared `model.default`.
+
+Bug: runway_video and higgsfield_video hardcoded stale model defaults in
+estimate_cost/estimate_runtime/execute (`gen4_turbo` / `kling_3.0`) while the
+schema advertised `seedance_2.0` as the premium default. Omitting `model` then
+quoted the wrong (cheap) model's cost and silently generated a different model
+than the schema promised — a Decision-Communication / cost-accuracy violation.
+"""
+
+import pytest
+
+import tools.video.higgsfield_video as higgsfield_video
+import tools.video.runway_video as runway_video
+from tools.video.higgsfield_video import HiggsFieldVideo
+from tools.video.runway_video import RunwayVideo
+
+
+@pytest.mark.parametrize(
+    "tool_cls, module",
+    [(RunwayVideo, runway_video), (HiggsFieldVideo, higgsfield_video)],
+)
+def test_default_model_constant_matches_schema(tool_cls, module):
+    # `execute()` reads `model` via `_DEFAULT_MODEL`; locking the constant to the
+    # schema default guards the silent-model-swap path without a network call.
+    schema_default = tool_cls().input_schema["properties"]["model"]["default"]
+    assert module._DEFAULT_MODEL == schema_default
+
+
+@pytest.mark.parametrize("tool_cls", [RunwayVideo, HiggsFieldVideo])
+def test_estimate_default_model_matches_schema(tool_cls):
+    tool = tool_cls()
+    schema_default = tool.input_schema["properties"]["model"]["default"]
+
+    # Cost/runtime with `model` omitted must equal the schema's declared default,
+    # not some stale hardcoded fallback.
+    assert tool.estimate_cost({}) == tool.estimate_cost({"model": schema_default}), (
+        f"{tool.name}.estimate_cost default model diverges from schema default "
+        f"{schema_default!r}"
+    )
+    assert tool.estimate_runtime({}) == tool.estimate_runtime({"model": schema_default}), (
+        f"{tool.name}.estimate_runtime default model diverges from schema default "
+        f"{schema_default!r}"
+    )

tools/video/higgsfield_video.py

@@ -24,6 +24,11 @@ from tools.base_tool import (
     ToolTier,
 )
 
+# Single source of truth for the default model. Referenced by both the input
+# schema and every code path that reads `model`, so estimate_cost / estimate_runtime
+# / execute can never silently diverge from the advertised default again.
+_DEFAULT_MODEL = "seedance_2.0"
+
 
 class HiggsFieldVideo(BaseTool):
     name = "higgsfield_video"
@@ -89,7 +94,7 @@ class HiggsFieldVideo(BaseTool):
                     "wan_2.5",
                     "soul_cinema",
                 ],
-                "default": "seedance_2.0",
+                "default": _DEFAULT_MODEL,
                 "description": "Underlying model. Defaults to Seedance 2.0 (preferred premium) — see .agents/skills/seedance-2-0/",
             },
             "duration": {
@@ -134,7 +139,7 @@ class HiggsFieldVideo(BaseTool):
         return ToolStatus.UNAVAILABLE
 
     def estimate_cost(self, inputs: dict[str, Any]) -> float:
-        model = inputs.get("model", "seedance_2.0")
+        model = inputs.get("model", _DEFAULT_MODEL)
         duration = int(inputs.get("duration", "5"))
         # Approximate per-clip costs based on Higgsfield credit pricing.
         # Seedance 2.0 on Higgsfield runs ~50-80 credits per 5s clip ≈ $0.50-$1.20.
@@ -151,7 +156,7 @@ class HiggsFieldVideo(BaseTool):
         return base * (duration / 5)
 
     def estimate_runtime(self, inputs: dict[str, Any]) -> float:
-        model = inputs.get("model", "seedance_2.0")
+        model = inputs.get("model", _DEFAULT_MODEL)
         if model in ("veo_3.1", "sora_2", "seedance_2.0"):
             return 120.0
         if model == "seedance_2.0_fast":
@@ -171,7 +176,7 @@ class HiggsFieldVideo(BaseTool):
         api_key, api_secret = creds
         start = time.time()
         operation = inputs.get("operation", "text_to_video")
-        model = inputs.get("model", "kling_3.0")
+        model = inputs.get("model", _DEFAULT_MODEL)
 
         payload: dict[str, Any] = {
             "prompt": inputs["prompt"],

tools/video/runway_video.py

@@ -47,6 +47,11 @@ _RUNTIME_SECONDS = {
     "seedance_2.0_fast": 60.0,
 }
 
+# Single source of truth for the default model. Referenced by both the input
+# schema and every code path that reads `model`, so estimate_cost / estimate_runtime
+# / execute can never silently diverge from the advertised default again.
+_DEFAULT_MODEL = "seedance_2.0"
+
 
 class RunwayVideo(BaseTool):
     name = "runway_video"
@@ -101,7 +106,7 @@ class RunwayVideo(BaseTool):
             "model": {
                 "type": "string",
                 "enum": ["seedance_2.0", "seedance_2.0_fast", "gen4_turbo", "gen4_aleph", "gen3a_turbo"],
-                "default": "seedance_2.0",
+                "default": _DEFAULT_MODEL,
                 "description": (
                     "seedance_2.0 = preferred premium default (single-pass synced audio, multi-shot, lip-sync — "
                     "Runway Unlimited/Enterprise plan, non-US only). "
@@ -149,12 +154,12 @@ class RunwayVideo(BaseTool):
         return os.environ.get("RUNWAY_API_KEY") or os.environ.get("RUNWAYML_API_SECRET")
 
     def estimate_cost(self, inputs: dict[str, Any]) -> float:
-        model = inputs.get("model", "gen4_turbo")
+        model = inputs.get("model", _DEFAULT_MODEL)
         duration = inputs.get("duration", 5)
         return _COST_PER_SECOND.get(model, 0.05) * duration
 
     def estimate_runtime(self, inputs: dict[str, Any]) -> float:
-        model = inputs.get("model", "gen4_turbo")
+        model = inputs.get("model", _DEFAULT_MODEL)
         return _RUNTIME_SECONDS.get(model, 30.0)
 
     def execute(self, inputs: dict[str, Any]) -> ToolResult:
@@ -168,7 +173,7 @@ class RunwayVideo(BaseTool):
         import requests
 
         start = time.time()
-        model = inputs.get("model", "gen4_turbo")
+        model = inputs.get("model", _DEFAULT_MODEL)
         operation = inputs.get("operation", "text_to_video")
         ratio_friendly = inputs.get("ratio", "16:9")
         ratio_pixels = _RATIO_MAP.get(ratio_friendly, "1280:720")