Serena

Instructions

Implementation Reference

• src/serena/tools/workflow_tools.py:70-84 (handler)

  The core handler implementation for the 'think_about_task_adherence' tool. It inherits from Tool and its apply() method returns a rendered prompt for task adherence reflection.
  class ThinkAboutTaskAdherenceTool(Tool): """ Thinking tool for determining whether the agent is still on track with the current task. """ def apply(self) -> str: """ Think about the task at hand and whether you are still on track. Especially important if the conversation has been going on for a while and there has been a lot of back and forth. This tool should ALWAYS be called before you insert, replace, or delete code. """ return self.prompt_factory.create_think_about_task_adherence()
• src/serena/generated/generated_prompt_factory.py:23-24 (helper)

  Helper method in the prompt factory that renders the specific prompt template used by the tool handler.
  def create_think_about_task_adherence(self) -> str: return self._render_prompt("think_about_task_adherence", locals())
• src/serena/tools/tools_base.py:355-427 (registration)

  The ToolRegistry dynamically discovers and registers all Tool subclasses, including ThinkAboutTaskAdherenceTool, by scanning subclasses in serena.tools packages. This makes the tool available for instantiation in the agent.
  @singleton class ToolRegistry: def __init__(self) -> None: self._tool_dict: dict[str, RegisteredTool] = {} for cls in iter_subclasses(Tool): if not any(cls.__module__.startswith(pkg) for pkg in tool_packages): continue is_optional = issubclass(cls, ToolMarkerOptional) name = cls.get_name_from_cls() if name in self._tool_dict: raise ValueError(f"Duplicate tool name found: {name}. Tool classes must have unique names.") self._tool_dict[name] = RegisteredTool(tool_class=cls, is_optional=is_optional, tool_name=name) def get_tool_class_by_name(self, tool_name: str) -> type[Tool]: return self._tool_dict[tool_name].tool_class def get_all_tool_classes(self) -> list[type[Tool]]: return list(t.tool_class for t in self._tool_dict.values()) def get_tool_classes_default_enabled(self) -> list[type[Tool]]: """ :return: the list of tool classes that are enabled by default (i.e. non-optional tools). """ return [t.tool_class for t in self._tool_dict.values() if not t.is_optional] def get_tool_classes_optional(self) -> list[type[Tool]]: """ :return: the list of tool classes that are optional (i.e. disabled by default). """ return [t.tool_class for t in self._tool_dict.values() if t.is_optional] def get_tool_names_default_enabled(self) -> list[str]: """ :return: the list of tool names that are enabled by default (i.e. non-optional tools). """ return [t.tool_name for t in self._tool_dict.values() if not t.is_optional] def get_tool_names_optional(self) -> list[str]: """ :return: the list of tool names that are optional (i.e. disabled by default). """ return [t.tool_name for t in self._tool_dict.values() if t.is_optional] def get_tool_names(self) -> list[str]: """ :return: the list of all tool names. """ return list(self._tool_dict.keys()) def print_tool_overview( self, tools: Iterable[type[Tool] | Tool] | None = None, include_optional: bool = False, only_optional: bool = False ) -> None: """ Print a summary of the tools. If no tools are passed, a summary of the selection of tools (all, default or only optional) is printed. """ if tools is None: if only_optional: tools = self.get_tool_classes_optional() elif include_optional: tools = self.get_all_tool_classes() else: tools = self.get_tool_classes_default_enabled() tool_dict: dict[str, type[Tool] | Tool] = {} for tool_class in tools: tool_dict[tool_class.get_name_from_cls()] = tool_class for tool_name in sorted(tool_dict.keys()): tool_class = tool_dict[tool_name] print(f" * `{tool_name}`: {tool_class.get_tool_description().strip()}") def is_valid_tool_name(self, tool_name: str) -> bool: return tool_name in self._tool_dict
• src/serena/mcp.py:252-259 (registration)

  Registers the tool instances as MCP tools by name in the FastMCP server, using a wrapper around the Tool's apply method.
  def _set_mcp_tools(self, mcp: FastMCP, openai_tool_compatible: bool = False) -> None: """Update the tools in the MCP server""" if mcp is not None: mcp._tool_manager._tools = {} for tool in self._iter_tools(): mcp_tool = self.make_mcp_tool(tool, openai_tool_compatible=openai_tool_compatible) mcp._tool_manager._tools[tool.get_name()] = mcp_tool log.info(f"Starting MCP server with {len(mcp._tool_manager._tools)} tools: {list(mcp._tool_manager._tools.keys())}")