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Think About Whether You Are Done

think_about_whether_you_are_done
Read-only

Check if you have completed the user's request by calling this tool to confirm task fulfillment before proceeding.

Instructions

Whenever you feel that you are done with what the user has asked for, it is important to call this tool.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The handler implementation: subclass of Tool with apply() method that returns a generated prompt instructing the agent to think about task completion.
    class ThinkAboutWhetherYouAreDoneTool(Tool):
        """
        Thinking tool for determining whether the task is truly completed.
        """
    
        def apply(self) -> str:
            """
            Whenever you feel that you are done with what the user has asked for, it is important to call this tool.
            """
            return self.prompt_factory.create_think_about_whether_you_are_done()
  • ToolRegistry singleton automatically discovers and registers all Tool subclasses (including ThinkAboutWhetherYouAreDoneTool) from serena.tools modules by deriving tool names via get_name_from_cls().
    @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)
  • Generated helper method in PromptFactory that renders the prompt template used by the tool's apply() method.
    def create_think_about_whether_you_are_done(self) -> str:
        return self._render_prompt("think_about_whether_you_are_done", locals())
  • Class method that derives the tool name 'think_about_whether_you_are_done' from the class name ThinkAboutWhetherYouAreDoneTool, used during registration.
    def get_name_from_cls(cls) -> str:
        name = cls.__name__
        if name.endswith("Tool"):
            name = name[:-4]
        # convert to snake_case
        name = "".join(["_" + c.lower() if c.isupper() else c for c in name]).lstrip("_")
        return name
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations indicate readOnlyHint=true and destructiveHint=false, which the description does not contradict. The description adds context by implying this is a self-assessment or decision-making tool, but it does not disclose additional behavioral traits like what triggers the 'done' state, any side effects, or how it interacts with other tools. With annotations covering safety, the description provides minimal extra value.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that directly states the tool's purpose and usage. It is front-loaded with the key information and contains no redundant or extraneous details, making it highly concise and well-structured for quick understanding.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 0 parameters, annotations covering read-only and non-destructive behavior, and an output schema exists, the description is reasonably complete. It explains when to use the tool, which is the core need. However, it could be more detailed about what 'done' entails or how it integrates with other tools, leaving minor gaps in full context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description does not add parameter details, which is appropriate. A baseline of 4 is applied since there are no parameters, and the description does not introduce unnecessary complexity.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states the tool's purpose as 'call this tool whenever you feel that you are done with what the user has asked for,' which is a clear action but vague in scope. It specifies a verb ('call') but lacks concrete details about what 'done' means or what resource it operates on, making it somewhat ambiguous compared to more specific sibling tools like 'create_text_file' or 'read_file'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance on when to use the tool: 'whenever you feel that you are done with what the user has asked for.' This gives a clear context for invocation. However, it does not mention when not to use it or name alternatives, such as sibling tools like 'think_about_collected_information' or 'think_about_task_adherence,' which might be relevant for related decision-making processes.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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