Enables intelligent and interactive feedback with users, designed to reduce premium OpenAI tool invocations by consolidating multiple requests into a single feedback-aware interaction.
Implements XML-based tool calling format for the interactive_feedback tool, enabling structured communication between the AI assistant and the MCP server.
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 MCP I've refactored the authentication module. Does this meet your requirements?"
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.
Developed by Pham Minh Nhat (nhatpm.lab) - nhatpm.lab@gmail.com
Maintained by Pham Minh Nhat (
nhatpm.lab)
📧 Email: nhatpm.lab@gmail.com
This project integrates Model Context Protocol (MCP) to ensure intelligent and interactive feedback with the user at critical points.
Related MCP server: MCP Feedback Collector
For the best results, add the following to your custom prompt in your AI assistant, you should add it on a rule or directly in the prompt (e.g., Cursor):
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.
This will ensure your AI assistant uses this MCP server to request user feedback before marking the task as completed.
By guiding the 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 (e.g., OpenAI tool invocations) on platforms like Cursor. 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.
This MCP server uses Qt's QSettings to store configuration on a per-project basis. This includes:
The command to run.
Whether to execute the command automatically on the next startup for that project (see "Execute automatically on next run" checkbox).
The visibility state (shown/hidden) of the command section (this is saved immediately when toggled).
Window geometry and state (general UI preferences).
These settings are typically stored in platform-specific locations (e.g., registry on Windows, plist files on macOS, configuration files in ~/.config or ~/.local/share on Linux) under an organization name "NhatPM" and application name "InteractiveFeedbackMCP", with a unique group for each project directory.
The "Save Configuration" button in the UI primarily saves the current command typed into the command input field and the state of the "Execute automatically on next run" checkbox for the active project. The visibility of the command section is saved automatically when you toggle it. General window size and position are saved when the application closes.
```json
{
"mcpServers": {
"interactive-feedback-mcp": {
"command": "uv",
"args": [
"--directory",
"/Users/nhatpm/Desktop/interactive-feedback-mcp",
"run",
"server.py"
],
"timeout": 600,
"autoApprove": [
"interactive_feedback"
]
}
}
}
```
* You might use a server identifier like `interactive-feedback-mcp` when configuring it in Cursor.Similar setup principles apply. You would configure the server command (e.g., uv run server.py with the correct --directory argument pointing to the project directory) in the respective tool's MCP settings, using interactive-feedback-mcp as the server identifier.
To run the server in development mode with a web interface for testing:
uv run fastmcp dev server.pyThis will open a web interface and allow you to interact with the MCP tools for testing.
Here's an example of how the AI assistant would call the interactive_feedback tool:
<use_mcp_tool>
<server_name>interactive-feedback-mcp</server_name>
<tool_name>interactive_feedback</tool_name>
<arguments>
{
"project_directory": "/path/to/your/project",
"summary": "I've implemented the changes you requested and refactored the main module."
}
</arguments>
</use_mcp_tool>For any questions, suggestions, or if you just want to share how you're using it, feel free to reach out on X!
Request interactive feedback for a given project directory and summary
| Name | Required | Description | Default |
|---|---|---|---|
| project_directory | Yes | Full path to the project directory | |
| summary | Yes | Short, one-line summary of the changes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'interactive feedback' but doesn't explain what that entails—whether it initiates a chat, sends a notification, requires user input, or has side effects like creating records. This leaves critical behavioral traits unspecified.
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?
The description is a single, direct sentence that efficiently conveys the core function without unnecessary words. It is front-loaded and appropriately sized for the tool's complexity, with no wasted verbiage.
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 the lack of annotations and output schema, the description is incomplete. It fails to explain what 'interactive feedback' means in practice, what the tool returns, or any behavioral nuances, leaving significant gaps for an AI agent to understand how to use it effectively.
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?
The schema description coverage is 100%, with clear descriptions for both parameters in the input schema. The description adds no additional meaning beyond the schema, such as format examples or constraints, but the schema adequately covers the basics, justifying the baseline score.
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?
The description clearly states the action ('Request interactive feedback') and specifies the target ('for a given project directory and summary'), making the purpose understandable. However, it doesn't distinguish from siblings since none exist, and 'interactive feedback' could be more specific about what type of feedback is being requested.
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?
The description provides no guidance on when to use this tool, such as appropriate contexts, prerequisites, or alternatives. It merely states what the tool does without indicating scenarios where it should or shouldn't be invoked.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v1.0.0interactive_feedbackWith only one tool, there is no possibility for ambiguity or overlap between tools. The tool's purpose is clearly distinct by default.
A single tool inherently has perfect naming consistency, as there are no other tools to compare against. The name 'interactive_feedback' follows a clear verb_noun pattern.
One tool is too few for a server named 'Interactive Feedback MCP', which suggests a broader scope for interactive feedback operations. This minimal set feels thin and incomplete for the implied domain.
The tool surface is severely incomplete for an interactive feedback domain. It only provides a request function, with no obvious operations for managing, updating, or retrieving feedback, creating significant gaps for agent workflows.
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