Interactive Feedback MCP
This server enables AI assistants to interactively check in with users during task execution via a UI dialog, reducing unnecessary speculative tool calls and improving efficiency.
Tool: interactive_feedback
Pops up a dialog to present a summary of work or ask the user a question. Requires:
project_directory: full path to the project or cwdsummary: a short summary of work done or the question to ask
Key capabilities:
Request user feedback: Collect real-time input or confirmation at any point during a task.
Ask questions: Pause and ask the user directly instead of making speculative tool calls.
Summarize work: Present completed or ongoing work for user review or approval.
Iterative interaction: Call the tool repeatedly until the user provides no further feedback, enabling a guided multi-step conversation.
Reduce costs: Consolidate multiple potential tool calls into a single feedback-aware request by confirming intent with the user before proceeding.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Interactive Feedback MCPAsk for my confirmation before proceeding with the code changes."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Interactive Feedback MCP
Why Use This?
By guiding the AI assistant to check in with the user instead of branching out into speculative, high-cost tool calls, this module can drastically reduce the number of premium requests. In some cases, it helps consolidate what would be up to 25 tool calls into a single, feedback-aware request — saving resources and improving performance.
Related MCP server: mcp-feedback-enhanced
Quick Install (Recommended)
One command to clone, install, and configure everything automatically for Antigravity IDE:
Windows (PowerShell):
irm https://raw.githubusercontent.com/nhatprx/Antigravity-MCP/v1.3.3/install.ps1 | iexLinux / macOS:
curl -fsSL https://raw.githubusercontent.com/nhatprx/Antigravity-MCP/v1.3.3/install.sh | bashThe installer will: clone the repo → install dependencies → configure MCP server → add coding rules. Just restart Antigravity after it finishes.
Manual Installation
Prerequisites
uv (Python package manager). Note: You do not need Python installed on your machine;
uvwill download and manage the required Python version automatically!Windows:
irm https://astral.sh/uv/install.ps1 | iexLinux/Mac:
curl -LsSf https://astral.sh/uv/install.sh | sh
Setup
Clone or download this repository.
Install dependencies:
cd path/to/interactive-feedback-mcp uv syncAdd the MCP server to your Antigravity configuration (
~/.gemini/antigravity/mcp.json):{ "mcpServers": { "interactive-feedback-mcp": { "command": "uv", "args": [ "--directory", "/path/to/interactive-feedback-mcp", "run", "server.py" ] } } }Note: If
uvis not in your system PATH, use the full path to theuvexecutable instead (e.g.,C:\\Users\\<user>\\AppData\\Local\\Python\\...\\Scripts\\uv.exe).
Prompt Engineering
For the best results, add the following as a coding rule in your AI assistant:
Whenever you want to ask a question, always call the MCP
interactive_feedback.
Whenever you're about to complete a user request, call the MCPinteractive_feedbackinstead of simply ending the process. Keep calling MCP until the user's feedback is empty, then end the request.
Adding Rules in Antigravity

Click Antigravity - Settings at the bottom of the chat panel.
In the Agent settings, click Manage next to Customizations.
Click + Global to add a global coding rule, then paste the prompt above.
Development
To run the server in development mode with a web interface for testing:
uv run fastmcp dev server.pyAuthor
Created by nhatprx.
License
This project is licensed under the MIT License.
Available Tools
1 toolinteractive_feedbackB
Request interactive feedback or ask the user a question by popping up a UI dialog.
| Name | Required | Description | Default |
|---|---|---|---|
| project_directory | Yes | Full path to the project directory or current working directory | |
| summary | Yes | Short summary of your work, OR the question you want to ask the user |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations; description only mentions popping up a UI dialog but omits critical behavioral traits such as whether it blocks, waits for response, or handles cancellation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, front-loaded with action. Efficient but could expand without losing conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and no annotations, description is too brief. Lacks details on usage behavior and parameter roles.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline is 3. Description adds no extra meaning to parameters beyond schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool's action: request interactive feedback or ask user a question via UI dialog. Verb and resource are specific.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when or when not to use the tool. No alternatives or context provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
1 tool update
v0.1.0- First observed
interactive_feedback
TDQS
Only one tool exists, so there is no possibility of confusion or overlap.
The single tool uses a clear verb_noun pattern ('interactive_feedback'), consistent and descriptive.
A server dedicated solely to interactive feedback is well-served by exactly one tool; the count matches the narrow scope.
For the server's stated purpose of requesting user feedback, the single tool covers the full functionality with no missing operations.
Maintenance
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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- -licenseBqualityNot gradedmaintenanceA Model Context Protocol server that enables AI assistants to request user feedback at critical points during interactions, improving communication and reducing unnecessary tool calls.13-
- FlicenseAqualityDmaintenanceEstablishes feedback-oriented development workflows with dual Web UI and Desktop interfaces, enabling AI to confirm with users before actions and consolidate multiple tool calls into a single feedback request.22-
- FlicenseAqualityDmaintenanceEstablishes feedback-oriented development workflows with Web UI and desktop app, enabling AI to confirm with users before actions and consolidate tool calls into feedback requests.2-
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