fix: parse dict strings from Robot Framework in ConfigUpdate

Robot Framework's Create Dictionary converts nested dicts like {'enabled': False} to string representation. Parse these strings before Pydantic validation. Also handle Pydantic models passed directly from unit tests.

src/server/models/config.py

@@ -289,6 +289,59 @@ class ConfigUpdate(BaseModel):
     scan_key_overrides: Optional[Dict[str, str]] = None
     other: Optional[Dict[str, Any]] = None
 
+    @classmethod
+    def _parse_dict_field(cls, v):
+        """Parse a field that may arrive as a malformed string from Robot Framework.
+
+        Robot Framework's Create Dictionary converts Python-style nested dicts
+        like {'enabled': False} into their string representation. Handle that here
+        before Pydantic's type validation runs. Also handles Pydantic models being
+        passed directly (from unit tests).
+        """
+        # Pydantic model - convert to dict first
+        if hasattr(v, 'model_dump'):
+            return v.model_dump()
+        if hasattr(v, 'dict'):
+            return v.dict()
+        # Already a dict
+        if isinstance(v, dict):
+            return v
+        # String - try parsing
+        if isinstance(v, str):
+            for parse_fn in (json.loads, ast.literal_eval):
+                try:
+                    parsed = parse_fn(v)
+                    if isinstance(parsed, dict):
+                        return parsed
+                except Exception:
+                    pass
+        return v
+
+    @model_validator(mode="before")
+    @classmethod
+    def _pre_validate(cls, data):
+        """Handle malformed dict strings from Robot Framework and Pydantic models passed directly.
+
+        Robot Framework's Create Dictionary converts Python-style nested dicts
+        like {'enabled': False} into their string representation.
+        Unit tests may pass Pydantic model instances directly.
+        Both cases need conversion before type validation.
+        """
+        if isinstance(data, dict):
+            data = dict(data)  # make mutable
+            for field in ("name", "data_dir", "scheduler", "logging", "backup", "nfo", "scan_key_overrides", "other"):
+                if field in data:
+                    v = data[field]
+                    # Pydantic model - convert to dict
+                    if hasattr(v, "model_dump"):
+                        data[field] = v.model_dump()
+                    # String from Robot Framework - try parsing
+                    elif isinstance(v, str):
+                        parsed = cls._parse_dict_field(v)
+                        if isinstance(parsed, dict):
+                            data[field] = parsed
+        return data
+
     def apply_to(self, current: AppConfig) -> AppConfig:
         """Return a new AppConfig with updates applied to the current config.