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Human-In-the-Loop MCP Server

Human-In-the-Loop MCP Server

by GongRzhe
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

get_user_choice

Presents a choice dialog that collects user selections from multiple options, enabling input for decisions or preferences.

Instructions

Create a choice dialog window for the user to select from multiple options.

This tool opens a GUI dialog box with a list of choices where the user can select one or multiple options. Perfect for getting decisions, preferences, or selections from the user.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "title": "<title>",
      "prompt": "<prompt>",
      "choices": "<choices>"
    },
    "name": "get_user_choice"
  }
}

Input Schema

NameRequiredDescriptionDefault
titleYesTitle of the choice dialog window
promptYesThe prompt/question to show to the user
choicesYesList of choices to present to the user
allow_multipleNoWhether user can select multiple choices

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.3

TDQS

A3.6/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden. It notes the tool opens a GUI dialog and supports single/multiple selection, but fails to disclose critical behaviors such as blocking nature, return format on cancel, or any side effects, leaving significant gaps.

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 two concise sentences, front-loading the purpose and then offering context. No unnecessary text; every sentence earns its place.

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

Completeness2/5

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

While an output schema exists (context signals), the description omits essential behavioral details like cancellation handling, blocking behavior, or return value structure, which are critical for a user-interaction tool with no annotations.

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

Parameters3/5

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

Schema coverage is 100%, with all parameters described. The description reiterates schema info (e.g., 'list of choices') without adding new semantic meaning. Baseline 3 is appropriate as the schema already documents the parameters adequately.

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 creates a choice dialog for user selection from multiple options, with explicit mention of single or multiple selection, distinguishing it from sibling tools like get_user_input (free text) and show_confirmation_dialog (yes/no).

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 advises it's 'perfect for getting decisions, preferences, or selections,' providing clear context for use. However, it lacks explicit guidance on when not to use it or mention of alternative tools for other input types.

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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