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mcp-pop-up

by GiantBeaver9
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Python
Local

mcp-pop-up

An MCP server that lets a local LLM ask you for guidance through a native desktop pop-up — just like the question prompts Claude shows in the app.

The model calls a single tool, ask_user, with a question and up to 6 suggested answers. An "Other" free-text choice is always added automatically, and the model decides whether you may pick one answer (radio buttons) or several (checkboxes). Your choice is returned to the model so it knows how to proceed.

Built for LM Studio, but it works with any MCP client that speaks stdio.

How it works

LM Studio ─stdio─► mcp_pop_up.server ─subprocess─► mcp_pop_up.dialog (tkinter)
   ▲                                                          │
   └──────────────  "The user selected: …"  ◄─────────────────┘

The server exposes the ask_user tool. When called, it launches the dialog in a short-lived subprocess to render the Tk window. Running the GUI in its own process keeps the blocking Tk event loop from colliding with the server's async loop, and works reliably across Windows, macOS, and Linux.

The code is split so each module has one job:

Module

Responsibility

mcp_pop_up/protocol.py

Request/result data model — validation, JSON (de)serialization, and result formatting. The single source of truth for the contract; depends on neither mcp nor tkinter.

mcp_pop_up/server.py

The FastMCP server and the ask_user tool.

mcp_pop_up/runner.py

Launches the dialog subprocess and parses its result.

mcp_pop_up/dialog/view.py

The PopupDialog tkinter window.

mcp_pop_up/dialog/__main__.py

Subprocess entry point: stdin → dialog → stdout.

server.py (root)

Thin launcher so clients can point at a file path without installing.

Related MCP server: Flag MCP

The ask_user tool

Argument

Type

Default

Description

question

string

—

The question shown to the user (required).

options

string[]

[]

Up to 6 suggested answers. May be empty for an open-ended question.

allow_multiple

boolean

false

true → checkboxes (pick several). false → radio buttons (pick one).

An "Other (type your own answer)" choice with a text box is always appended, so the user is never limited to the options you provide.

Returns a short human-readable summary, e.g.:

  • The user selected: Wait for review

  • The user selected 2 options:\n- Lint\n- Build

  • The user selected: Refactor the parser first (typed into "Other")

  • The user cancelled the pop-up without choosing an answer.

Requirements

  • Python 3.10+

  • tkinter — bundled with the standard Python installers on Windows and macOS. On Linux install it separately:

    sudo apt install python3-tk      # Debian/Ubuntu
sudo dnf install python3-tkinter # Fedora

  • The mcp Python SDK (installed below).

Install

git clone https://github.com/GiantBeaver9/mcp-pop-up.git
cd mcp-pop-up
pip install -r requirements.txt        # just the runtime dep, or:
pip install -e .                        # installs the `mcp-pop-up` command too

Verify it starts (Ctrl-C to stop — it waits silently for a client on stdio):

python server.py          # via the file-path launcher, or:
python -m mcp_pop_up       # via the package, or:
mcp-pop-up                 # via the console script (after `pip install -e .`)

Configure LM Studio

LM Studio manages MCP servers through its mcp.json file (Program → Edit mcp.json, or the "Integrations" / MCP settings panel). Add this entry, using an absolute path to the launcher:

{
  "mcpServers": {
    "pop-up": {
      "command": "python",
      "args": ["/absolute/path/to/mcp-pop-up/server.py"]
    }
  }
}

On Windows, use python (or the full path to python.exe) and a full path such as C:\\Users\\you\\mcp-pop-up\\server.py.

The root server.py launcher works without installing the package. If you ran pip install -e ., you can instead use "command": "mcp-pop-up", "args": [], or "args": ["-m", "mcp_pop_up"].

Reload the MCP servers in LM Studio. The ask_user tool should now appear and be available to the model. Ask your model something like "Ask me whether to deploy now or wait, then act on my answer" to see the pop-up.

Tip: Make sure LM Studio runs the same Python interpreter that has both mcp and tkinter installed — the server launches the dialog with that same interpreter.

Use with other MCP clients

Any stdio MCP client works. Point it at python /absolute/path/to/server.py.

Project layout

mcp-pop-up/
├── server.py                 # thin launcher (point MCP clients here)
├── pyproject.toml            # packaging + `mcp-pop-up` console script
├── requirements.txt          # runtime dependency (mcp)
└── mcp_pop_up/
    ├── __main__.py           # `python -m mcp_pop_up` → server
    ├── protocol.py           # request/result model: validation + JSON + formatting
    ├── server.py             # FastMCP server + the `ask_user` tool
    ├── runner.py             # launches the dialog subprocess, parses its result
    └── dialog/
        ├── __main__.py       # subprocess entry: stdin → dialog → stdout
        └── view.py           # PopupDialog tkinter window

Troubleshooting

  • GUI is unavailable: No module named 'tkinter' — install tkinter for the interpreter LM Studio uses (see Requirements).

  • No window appears — the pop-up shows on the machine running the server. It needs a desktop session; it won't display over a headless SSH connection.

  • Tool doesn't show up in LM Studio — double-check the absolute path in mcp.json and reload the MCP servers.

Available Tools

1 tool
ask_userA

Ask the user for guidance via a desktop pop-up and return their answer.

Use this whenever you need a decision, clarification, or direction from the person you are helping instead of guessing. A native window appears with your question and buttons for each answer. An "Other" choice with a free-text box is always added, so the user is never boxed in by your options.

Args: question: The question to show the user. Be specific and self-contained. options: Up to 6 suggested answers. May be empty to ask an open-ended question (the user then answers via the "Other" text box). allow_multiple: If True the user may select several answers (checkboxes); if False they pick exactly one (radio buttons). Default False.

Returns: A short human-readable summary of what the user chose, or a note that the prompt was cancelled.

ParametersJSON Schema
NameRequiredDescriptionDefault
optionsNo
questionYes
allow_multipleNo

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

A4.8/5.0
Behavior5/5

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

With no annotations provided, the description fully discloses behavior: a pop-up appears, an 'Other' option is always added, and the return is a summary or cancellation note. It also explains UI behavior based on parameters (radio vs checkboxes). This is comprehensive and transparent.

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 concise yet complete, with a clear structure: a one-sentence summary, a usage note, then a bulleted Args/Returns section. Every sentence adds value, and the information is front-loaded.

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 has 3 parameters, no annotations, and an output schema exists (though not provided), the description covers all necessary aspects: purpose, usage, parameter details, and return value. There are no gaps in context for selecting and invoking this tool.

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?

The input schema only provides names and types (0% coverage), but the description adds valuable semantics: question should be specific and self-contained, options can be up to 6 or empty for open-ended questions, allow_multiple controls checkbox vs radio behavior. This fully compensates for the schema's lack of descriptions.

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 identifies the action ('ask the user for guidance via a desktop pop-up') and the resource (the user's answer). It is specific and distinguishes this from guessing, which is the only alternative mentioned. No siblings exist, so differentiation is not required.

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 states when to use: 'whenever you need a decision, clarification, or direction from the person you are helping instead of guessing.' It does not specify when not to use, but given no sibling tools and a clear purpose, this is sufficient. A slight gap in exclusion criteria prevents a 5.

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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TDQS

A4.9/5.0
Disambiguation5/5

Only one tool exists, so there is no possibility of confusion or overlap with other tools.

Naming Consistency5/5

With a single tool, naming is trivially consistent. The name 'ask_user' clearly indicates its purpose.

Tool Count5/5

One tool is ideal for this focused purpose—asking the user for guidance. Adding more tools would be unnecessary.

Completeness5/5

The tool fully covers the server's purpose: it can ask questions with multiple choice or free-text answers, and handles cancellation. No gaps are apparent.

Maintenance

ActivityStale
ResponsivenessSyncing

Resources

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

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