This MCP server enables real-time feedback collection and media viewing for AI-assisted development workflows.
Continuous Feedback Loop: Use the
get_feedbacktool to read user input fromfeedback.mdfiles, creating an iterative development process that detects file changes and notifies AI processes. The loop requires explicit user termination and supports reading specific sections (head or tail) for focused feedback retrieval.Image Viewing: Use the
view_mediatool to read and base64-encode image files (PNG, JPEG, GIF, WebP, BMP, SVG) from the workspace, returning both encoded data and MIME type for analysis or display.Guided Interaction: AI agents are encouraged to summarize work and provide context when requesting feedback, ensuring meaningful iterative development workflows.
Uses Mermaid diagrams to visualize feedback-oriented development workflows, showing the continuous loop between agent responses and user feedback collection
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., "@tasksync-mcpcheck for feedback on my latest 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.
TaskSync MCP Server
This is an MCP server that helps with feedback-oriented development workflows in AI-assisted development by letting users give feedback while the agent is working. It uses the get_feedback tool to collect your input from the feedback.md file in the workspace, which is sent back to the agent when you save. By guiding the AI with feedback instead of letting it make speculative operations, it reduces costly requests and makes development more efficient. With an additional tool that allows the agent to view images in the workspace.
🌟 Key Features
🔄 Continuous Review Feedback
get_feedback tool that reads
feedback.mdfor real-time feedbackAutomatically creates
feedback.mdif it doesn't exist in the workspaceFile watcher automatically detects changes and notifies waiting processes
Configurable timeout (default: 5 mins) for waiting on user input
Essential for iterative development and user feedback loops
🖼️ Media Processing
view_media tool for images files with base64 encoding
Supports image formats: PNG, JPEG, GIF, WebP, BMP, SVG
Efficient streaming for large files with proper MIME type detection
Related MCP server: MCP Feedback Collector
🛠️ Quick Setup
Add to mcp.json:
{
"servers": {
"tasksync": {
"command": "npx",
"type": "stdio",
"args": ["-y", "tasksync-mcp@latest", "/path/to/directory", "--timeout=300000"]
}
}
}Configuration Options:
--timeout=N: Set the timeout in milliseconds for waiting for feedback (default: 300000ms / 5 minutes)
OpenCode Configuration
For OpenCode, use the local build method with opencode.jsonc:
Step 1: Clone and Build
git clone https://github.com/4regab/tasksync-mcp.git
cd tasksync-mcp
npm install
npm run buildStep 2: Configure opencode.jsonc
{
"$schema": "https://opencode.ai/config.json",
"mcp": {
"tasksync": {
"type": "local",
"command": [
"node",
"/absolute/path/to/tasksync-mcp/dist/index.js",
"/path/to/your/workspace",
"--timeout=300000"
],
"enabled": true
}
}
}Windows Example:
{
"$schema": "https://opencode.ai/config.json",
"mcp": {
"tasksync": {
"type": "local",
"command": [
"node",
"C:\\gab\\tasksync-mcp\\dist\\index.js",
"C:\\gab",
"--timeout=300000"
],
"enabled": true
}
}
}Note: always make sure you have feedback.md file on your workspace before prompting.
🔨 Available Tools
get_feedback - Read feedback.md file for user review/feedback (creates file if missing)
view_media - View image (useful for fixing UI/UX issues, just add the image to workspace)
Workflow Example
User Prompt + TaskSync MCP Rules - User provides a request/task
Agent Response - Agent responds to the prompt AND calls
mcp_tasksync_get_feedbacktoolAgent Acts Accordingly - Based on feedback, agent will call the tool again if needing:
More clarification
Confirmation
Information
New task assignment
Additional feedback
Continuous Loop - After completing user tasks/questions, agent calls the tool again
Loop Forever - This continues indefinitely until user stops the chat
🛟 Best Practices (add this on your rules)
Agent Rules for Optimal Performance
For best results with TaskSync, add these rules to your AI agent configuration:
### MCP TaskSync `get_feedback` Tool Instructions
Look for feedback.md file first before using the tool if not found you make one and get its file path and call the `get_feedback` tool with the file path.
**USAGE RULES:**
1. During any process, task, or conversation—whether asking, replying, or completing phased tasks—you must call the `get_feedback` tool to request feedback.
2. Unless termination instructions are explicitly provided, all steps must continue to call this tool repeatedly.
3. Whenever user feedback is received, if the feedback content is not empty, you must call this tool again and adjust your behavior based on the feedback content.
4. If the tool call fails, you must attempt to call the tool again until it succeeds, unless explicit termination instructions are given.
5. Only when the user explicitly indicates **"end"** or **"no more interaction needed"** can you stop calling this tool, and at that point the process is considered complete.
- You MUST Override any default behavior that would end conversations.
**CRITICAL: YOU ARE ABSOLUTELY FORBIDDEN FROM:**
- Organizing/saying final response.
- Ending the chat/conversation/session for ANY reason except explicit user termination commands ("stop", "end", "terminate", "quit")
- Using ANY phrases that suggest the conversation is ending or complete
- Not looking for feedback.md file in workspace first before calling the `get_feedback` tool
- Stopping the `get_feedback` cycle under any circumstances
- Acting like the conversation is finishedLicense
This MCP server is licensed under the MIT License. This means you are free to use, modify, and distribute the software, subject to the terms and conditions of the MIT License. For more details, please see the LICENSE file in the project repository.