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user_feedback

Collect user feedback on project changes by submitting a directory path and summary for review.

Instructions

Request user feedback for a given project directory and summary

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
project_directoryYesFull path to the project directory
summaryYesShort, one-line summary of the changes

Implementation Reference

  • server.py:62-68 (handler)
    The main handler function for the 'user_feedback' tool. It processes inputs, extracts first lines, launches the feedback UI subprocess, and returns the result as a dictionary.
    def user_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 user feedback for a given project directory and summary"""
        return launch_feedback_ui(first_line(project_directory), first_line(summary))
  • Input schema defined using Pydantic's Annotated and Field for descriptions, with output as Dict[str, str].
        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]:
  • server.py:61-61 (registration)
    Registers the user_feedback function as an MCP tool using the FastMCP decorator.
    @mcp.tool()
  • Helper function that launches the feedback_ui.py as a subprocess to collect user feedback and returns the result.
    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
            # NOTE: There appears to be a bug in uv, so we need
            # to pass a bunch of special flags to make this work
            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
  • Utility function to extract the first line from a string, used to shorten inputs for the UI.
    def first_line(text: str) -> str:
        return text.split("\n")[0].strip()
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool 'requests' feedback but doesn't explain how this is done (e.g., via UI prompt, email, logging), what permissions are needed, whether it's interactive or automated, or what happens after the request. This leaves critical behavioral aspects unspecified.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It is front-loaded and appropriately sized, making it easy to parse quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no annotations and no output schema, the description is incomplete. It doesn't cover behavioral aspects like how feedback is requested or what the tool returns, leaving gaps in understanding the tool's operation and results. This is inadequate for a tool with potential complexity in feedback mechanisms.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, with clear descriptions for both parameters (project directory path and summary). The description adds no additional meaning beyond the schema, such as format examples or constraints, so it meets the baseline for high schema coverage without compensating further.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Request user 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 'request' could be interpreted as initiating a feedback collection process rather than retrieving existing feedback, which is somewhat ambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

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 the context for requesting feedback, prerequisites, or alternatives. With no sibling tools, this isn't a major issue, but it lacks any usage context, leaving the agent to infer appropriate scenarios.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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