interactive_feedback

Instructions

Implementation Reference

• interactive_feedback_mcp/server.py:65-73 (handler)

  The core handler function for the 'interactive_feedback' tool. Registered via @mcp.tool() decorator. Input schema defined via Annotated with Field descriptions. Launches UI subprocess and processes result.

  @mcp.tool()
  def interactive_feedback(
      project_directory: Annotated[str, Field(description="Full path to the project directory")],
      summary: Annotated[str, Field(description="Short, one-line summary of the changes")],
  ) -> Dict[str, str]:
      """Request interactive feedback for a given project directory and summary"""
      
      result_dict = launch_feedback_ui(first_line(project_directory), first_line(summary))
      return header_data(result_dict)

• interactive_feedback_mcp/server.py:21-60 (helper)

  Helper function that launches the feedback_ui.py subprocess, passing project_directory and summary, captures JSON output.

  def launch_feedback_ui(project_directory: str, summary: str) -> dict[str, str]:
      # Create a temporary file for the feedback result
      with tempfile.NamedTemporaryFile(suffix=".json", delete=False) as tmp:
          output_file = tmp.name
  
      try:
          # Get the path to feedback_ui.py relative to this script
          script_dir = os.path.dirname(os.path.abspath(__file__))
          feedback_ui_path = os.path.join(script_dir, "feedback_ui.py")
  
          # Run feedback_ui.py as a separate process
          args = [
              sys.executable,
              "-u",
              feedback_ui_path,
              "--project-directory", project_directory,
              "--prompt", summary,
              "--output-file", output_file
          ]
          result = subprocess.run(
              args,
              check=False,
              shell=False,
              stdout=subprocess.DEVNULL,
              stderr=subprocess.DEVNULL,
              stdin=subprocess.DEVNULL,
              close_fds=True
          )
          if result.returncode != 0:
              raise Exception(f"Failed to launch feedback UI: {result.returncode}")
  
          # Read the result from the temporary file
          with open(output_file, 'r') as f:
              result = json.load(f)
          os.unlink(output_file)
          return result
      except Exception as e:
          if os.path.exists(output_file):
              os.unlink(output_file)
          raise e

• interactive_feedback_mcp/server.py:110-131 (helper)

  Helper function that processes the result from UI: extracts logs, feedback text, compresses and converts uploaded images to MCP Image objects, returns as tuple.

  def header_data(result_dict: dict) -> tuple:
      # print(result_dict)
      # {'logs': '', 'interactive_feedback': 'asdasdas', 'uploaded_images': ['/Users/ll/Desktop/2025/interactive-feedback-mcp/images/feedback.png']}
  
      logs = result_dict.get("logs", "")
      interactive_feedbacktxt = result_dict.get("interactive_feedback", "")
      uploaded_images = result_dict.get("uploaded_images", [])
  
      processed_content: List[Union[str, Image]] = []
  
      if logs and logs != "":
          processed_content.append("收集的日志: \n" + logs)
      
      if interactive_feedbacktxt and interactive_feedbacktxt != "":
          processed_content.append("用户反馈信息: \n" + interactive_feedbacktxt)
      
      for image_path in uploaded_images:
          image_data,img_format = compress_image(image_path)
          mcp_image = Image(data=image_data, format=img_format)
          processed_content.append(mcp_image)
          
      return tuple(processed_content)

• interactive_feedback_mcp/server.py:85-108 (helper)

  Helper for compressing images if >800KB, thumbnails if needed, returns image data and format.

  def compress_image(image_path: str):
      pil_img = PILImage.open(image_path)
      img_format = (pil_img.format or "PNG").lower()
      temp_image_path = image_path + ".temp.jpg"
      if os.path.getsize(image_path) > 800 * 1024:
          img_format = (pil_img.format or "jpg").lower()
  
          pil_img.save(temp_image_path, optimize=True, quality=50)
          # 输出现在的尺寸
          # print(f"压缩前大小: {friendly_size(os.path.getsize(image_path))}")
          # print(f"压缩后大小: {friendly_size(os.path.getsize(temp_image_path))}")
          if os.path.getsize(temp_image_path) > 800 * 1024:
              pil_img.thumbnail((512, 512))
              pil_img.save(temp_image_path, optimize=True, quality=50)
              # print(f"缩放后大小: {friendly_size(os.path.getsize(temp_image_path))}")
              # print(f"缩放后大小: {friendly_size(os.path.getsize(temp_image_path))}")
          with open(temp_image_path, "rb") as f:
              image_data = f.read()
          os.unlink(temp_image_path)
          return image_data,img_format
      else:
          with open(image_path, "rb") as f:
              image_data = f.read()
          return image_data, img_format

• interactive_feedback_mcp/server.py:19-19 (registration)

  FastMCP server instance creation, required for tool registration via decorators.

  mcp = FastMCP("Interactive Feedback MCP", log_level="ERROR")