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kerodkibatu
by kerodkibatu

implore

Request human input through interactive GUI dialogs with quiz-style questions to clarify requirements, get decisions, or extract knowledge using multiple choice and free-form responses.

Instructions

This tool launches a separate GUI process to show a quiz with one or more questions and waits for the user to respond. Multiple choice questions include an automatic "Other..." option with text input for additional flexibility. Perfect for clarifying requirements, getting decisions, or extracting implicit knowledge from users.

Args: questions: A list of question objects. Each question object should have: - text (str): The question text - type (str): Either "multiple_choice" or "free_form". For "multiple_choice", an automatic "Other..." radio button with text input is included after the options. - options (list, optional): List of options for multiple choice questions - id (str, optional): Unique identifier (auto-generated as "q1", "q2", etc. if not provided) title: The title of the dialog window (default: "Human Input Requested")

Returns: Dictionary with structured response: - On success: {"success": True, "answers": {question_id: answer, ...}} - On cancel: {"success": False, "cancelled": True} - On error: {"success": False, "error": "error message"}

Notes:

  • Unanswered multiple choice questions return null

  • Unanswered free-form questions return empty string ""

  • Prefer using comprehensive multiple choice options for most questions to provide structured choices, reserving free-form for simple copy-paste values or easily answered open questions. The automatic "Other..." option in multiple choice provides flexibility for cases not covered by the options.

Examples: Single question: implore(questions=[ { "id": "api_key", "text": "What is your API key?", "type": "free_form" } ])

Multiple questions:
implore(questions=[
    {
        "id": "framework",
        "text": "Which framework should we use?",
        "type": "multiple_choice",
        "options": ["React", "Vue", "Angular"]
    },
    {
        "id": "requirements",
        "text": "Any additional requirements?",
        "type": "free_form"
    }
])

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
questionsYes
titleNoHuman Input Requested
Behavior5/5

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

With no annotations provided, the description carries the full burden and does an excellent job. It discloses key behavioral traits: that it launches a GUI process (blocking/non-blocking behavior implied by 'waits'), includes automatic 'Other...' option functionality, describes three possible return states (success, cancel, error), and explains handling of unanswered questions. This goes well beyond what a basic description would provide.

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

Conciseness4/5

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

The description is well-structured with clear sections (purpose, args, returns, notes, examples) and front-loads the core functionality. While comprehensive, some sentences could be more concise (e.g., the notes section has some redundancy). Overall, most content earns its place by providing essential information.

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

Completeness5/5

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

Given the tool's complexity (interactive GUI, multiple question types, structured responses) and complete lack of annotations and output schema, the description provides excellent completeness. It covers purpose, parameters, return values, behavioral notes, and examples - everything needed to understand and use this tool effectively.

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

Parameters5/5

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

With 0% schema description coverage, the description fully compensates by providing detailed parameter documentation. It explains the 'questions' parameter structure including required fields (text, type), optional fields (options, id), valid type values, and the automatic 'Other...' behavior. It also documents the 'title' parameter default value. This adds substantial meaning beyond the bare schema.

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

Purpose5/5

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

The description clearly states the tool's purpose with specific verbs ('launches a separate GUI process', 'shows a quiz', 'waits for user to respond') and resources ('quiz with one or more questions'). It distinguishes itself by being the only interactive user-input tool in this context, as there are no sibling tools mentioned.

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 clear context for when to use this tool ('Perfect for clarifying requirements, getting decisions, or extracting implicit knowledge from users') and includes usage notes about preferring multiple choice with 'Other...' options. However, it doesn't explicitly state when NOT to use it or mention alternatives since there are no sibling tools.

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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