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Sighthesia

interactive-choice-mcp

by Sighthesia

provide_choice

Presents interactive choice prompts to users for decision-making, such as confirming high-risk actions or validating task completion. Returns the user's selection.

Instructions

Use this tool to resolve ambiguities, confirm high-risk actions, or validate task completion.

USAGE RULES:
1. SUMMARY: The `prompt` argument MUST follow this structure:
   - A concise summary of actions taken and files modified.
   - What you need the user to decide or if they are satisfied with the result.
2. FEEDBACK INTEGRATION: If a user provides `option_annotations` or `additional_annotation`, treat them as new instructions, execute the changes, and call this tool AGAIN to re-verify.

TERMINAL WORKFLOW:
- If `action_status` == "pending_terminal_launch":
    1. IMMEDIATELY execute the `terminal_command` using the terminal tool.
    2. Monitor output for `[SWITCH_TO_WEB] session_id=xxx`. If found, call `poll_selection(session_id)`.
    3. If `[SELECTION_COMPLETE]` appears, parse the result and proceed.

Args:
    selection_mode: "single" or "multi".
    options: List of dicts (id, description). Always include a `recommended: true` option.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
titleYes
promptYes
optionsYes
selection_modeYes
Behavior4/5

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

No annotations exist, so description carries full burden. It transparently explains that the tool presents choices to the user, requires specific arguments, and triggers terminal commands if action_status is pending. However, it does not explicitly state whether the tool is non-destructive or has side effects.

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 (USAGE RULES, TERMINAL WORKFLOW, Args) and front-loaded purpose. It is slightly verbose but every sentence serves a purpose, effectively communicating complex workflows.

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 no output schema and missing schema descriptions, the description is comprehensive: it covers tool purpose, usage rules, parameter details, terminal workflow integration, and links to sibling poll_selection, enabling correct agent invocation.

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?

With 0% schema description coverage, the description compensates by detailing the prompt structure (summary + decision request), options format (list of dicts with id/description, include recommended), and selection_mode values. It adds significant meaning beyond the schema, though options structure could be more precise.

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 explicitly states the tool's purpose: 'resolve ambiguities, confirm high-risk actions, or validate task completion.' This is a specific verb-resource combination that clearly distinguishes it from sibling poll_selection, which polls for selections.

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

Usage Guidelines5/5

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

Detailed USAGE RULES, FEEDBACK INTEGRATION, and TERMINAL WORKFLOW provide explicit guidance on when and how to use the tool, including scenarios like pending_terminal_launch and integration with poll_selection.

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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