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animalnots

Human-In-the-Loop MCP Server

by animalnots

get_user_input

Opens a dialog window to request text, numbers, or other data from the user, enabling clarification or input collection during AI interactions.

Instructions

Create an input dialog window for the user to enter text, numbers, or other data.

This tool opens a GUI dialog box where the user can input information that the LLM needs. Perfect for getting specific details, clarifications, or data from the user.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
titleYesTitle of the input dialog window
promptYesThe prompt/question to show to the user
default_valueNoDefault value to pre-fill in the input field
input_typeNoType of input expectedtext

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

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 mentions a GUI dialog box and user interaction but does not disclose behavioral traits such as whether it blocks execution, timeout behavior, or what happens if the user cancels.

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 concise with a clear front-loaded purpose. However, the second line ('This tool opens a GUI dialog box...') slightly repeats the first sentence, making it minimally less efficient.

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?

Given the presence of an output schema and full parameter documentation, the description covers the basic purpose adequately. But it lacks details on error handling, cancellation, or return format, which is a gap for a user-interaction tool.

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 description coverage is 100%, and the schema documents each parameter well. The description adds no extra context beyond the schema, so it meets the baseline but does not enhance understanding.

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

Purpose4/5

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

The description clearly identifies the tool as creating an input dialog for user data entry, using a specific verb and resource. It distinguishes from siblings like get_user_choice and get_multiline_input by mentioning text, numbers, and other data, but does not explicitly contrast them.

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

Usage Guidelines3/5

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

The description states it's 'Perfect for getting specific details, clarifications, or data from the user,' which implies suitable use cases but does not specify when not to use it or provide alternatives like get_multiline_input for multi-line input.

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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