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Human-In-the-Loop MCP Server

Human-In-the-Loop MCP Server

by GongRzhe
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

get_multiline_input

Collect detailed user input through a multi-line text dialog, ideal for obtaining code, descriptions, or long-form content.

Instructions

Create a multi-line text input dialog for the user to enter longer text content.

This tool opens a GUI dialog box with a large text area where the user can input multiple lines of text. Perfect for getting detailed descriptions, code, or long-form content.

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>"
    },
    "name": "get_multiline_input"
  }
}

Input Schema

NameRequiredDescriptionDefault
titleYesTitle of the input dialog window
promptYesThe prompt/question to show to the user
default_valueNoDefault text to pre-fill in the text area

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 provided, so description must cover behavior. It mentions opening a GUI dialog but omits key details: whether it blocks, if cancellable, what happens on cancel/close, any side effects, or the return format. With output schema, return structure may be covered, but behavioral context is insufficient.

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?

Two paragraphs: first defines purpose, second adds usage context. No wasted words, but slightly redundant. Could be more concise without losing clarity.

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

Completeness3/5

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

For a simple dialog tool with 3 parameters and an output schema, the description is adequate but lacks behavioral details (e.g., modal/blocking, cancellation behavior). Sibling differentiation is implicit rather than explicit. Meets minimum but has gaps.

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 good descriptions. The tool description does not add meaning beyond the schema; it only restates 'title' and 'prompt' in usage context. Baseline 3 applies.

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?

Description clearly states the tool creates a multi-line text input dialog for longer text, distinguishing it from siblings like get_user_input (single line) and get_user_choice.

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?

Explicitly says 'Perfect for getting detailed descriptions, code, or long-form content,' which implies when to use, but lacks explicit when-not-to-use or alternative mentions. Sibling names provide implicit guidance.

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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