added file permissions and fixed a duplicate output bug.

app/tools/base.py

@@ -0,0 +1,81 @@
+"""BaseTool ABC — foundation for all agent-callable tools."""
+
+import logging
+from abc import ABC, abstractmethod
+from pathlib import Path
+from typing import Any, ClassVar
+
+from pydantic import BaseModel, ValidationError
+
+from app.models.config import AppConfig
+from app.models.tool_call import ToolResult, ToolResultStatus
+
+logger = logging.getLogger(__name__)
+
+
+class BaseTool(ABC):
+    """Abstract base class for all agent tools.
+
+    Subclasses must set the class-level ``name``, ``description``, and
+    ``params_model`` attributes and implement ``execute``.
+    """
+
+    name: ClassVar[str]
+    description: ClassVar[str]
+    params_model: ClassVar[type[BaseModel]]
+
+    def __init__(self, workspace_root: Path, config: AppConfig) -> None:
+        self.workspace_root = workspace_root
+        self.config = config
+        self.logger = logging.getLogger(f"{__name__}.{self.name}")
+
+    @abstractmethod
+    def execute(self, *, tool_call_id: str, **kwargs: Any) -> ToolResult:
+        """Execute the tool with validated parameters.
+
+        Subclasses implement the actual tool logic here.
+        """
+
+    def run(self, tool_call_id: str, arguments: dict[str, Any]) -> ToolResult:
+        """Public entry point: validate arguments, execute, guarantee a ToolResult.
+
+        Never raises — all exceptions are caught and returned as error results.
+        """
+        try:
+            validated = self.params_model(**arguments)
+        except ValidationError as exc:
+            self.logger.warning("Validation error for %s: %s", self.name, exc)
+            return ToolResult(
+                tool_call_id=tool_call_id,
+                tool_name=self.name,
+                status=ToolResultStatus.ERROR,
+                error=f"Invalid arguments: {exc}",
+            )
+
+        try:
+            return self.execute(tool_call_id=tool_call_id, **validated.model_dump())
+        except Exception as exc:
+            self.logger.exception("Unexpected error in tool %s", self.name)
+            return ToolResult(
+                tool_call_id=tool_call_id,
+                tool_name=self.name,
+                status=ToolResultStatus.ERROR,
+                error=f"Tool execution failed: {exc}",
+            )
+
+    def get_openai_schema(self) -> dict[str, Any]:
+        """Return the OpenAI function-calling schema for this tool."""
+        schema = self.params_model.model_json_schema()
+        # Remove the top-level title/description that Pydantic adds —
+        # those belong on the function object, not the parameters.
+        schema.pop("title", None)
+        schema.pop("description", None)
+
+        return {
+            "type": "function",
+            "function": {
+                "name": self.name,
+                "description": self.description,
+                "parameters": schema,
+            },
+        }