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.
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Tools
Related MCP Servers
- -licenseBquality-maintenanceA Model Context Protocol server that enables AI assistants to request user feedback at critical points during interactions, improving communication and reducing unnecessary tool calls.Last updated13
- 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.Last updated22
- 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.Last updated2
- FlicenseAqualityBmaintenanceEstablishes feedback-oriented development workflows with Web UI and desktop application interfaces, enabling AI to confirm with users and consolidate tool calls into single feedback requests.Last updated2
Related MCP Connectors
Human-in-the-loop for AI coding agents — ask questions, get approvals via Slack.
Runtime permission, approval, and audit layer for AI agent tool execution.
See, price, and control every tool call your AI agents make: policy checks, cost, and audit tools.
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/nhatprx/antigravity-mcp'
If you have feedback or need assistance with the MCP directory API, please join our Discord server