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mowan-mcp-feedback

mowan-mcp-feedback

by limowan
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

interactive_feedback

Collect interactive user feedback via a web UI, enabling users to review AI work summaries, type comments, and upload supporting images.

Instructions

Collect interactive feedback from users via a Web UI.

Opens a browser-based interface where users can review the AI's work summary, type text feedback, and upload images.

Args: project_directory: Project directory path for context. summary: Markdown-formatted summary of AI work for user review. timeout: Seconds to wait for user feedback (default 600).

Returns: ToolResult: TextContent with user's text feedback and MCPImage objects for any uploaded images.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "interactive_feedback"
  }
}

Input Schema

NameRequiredDescriptionDefault
summaryNoAI 工作完成的摘要说明我已完成了您请求的任务。
timeoutNo等待用户反馈的超时时间（秒）
project_directoryNo项目目录路径.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.2

TDQS

A4.5/5.0
Behavior4/5

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 explains that a browser-based UI is opened, that it waits for user feedback with a timeout, and that it returns both text and image feedback. This is reasonably transparent, though it does not detail potential side effects like blocking or required permissions.

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 well-structured with a clear summary sentence, followed by a brief Args and Returns section. Every sentence serves a purpose. The front-loaded summary gives immediate understanding of the tool's function.

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

Completeness5/5

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

The tool has no output schema, but the description provides a Returns section that explains the ToolResult structure with TextContent and MCPImage objects. Combined with parameter details and the user flow, the description is complete for a tool of this complexity.

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

Parameters4/5

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

The schema description coverage is 100%, so the baseline is 3. The description adds valuable meaning beyond the schema by clarifying that 'summary' is Markdown-formatted, that 'project_directory' provides context, and that 'timeout' waits for feedback. This enhances the schema's basic definitions.

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

Purpose5/5

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

The description clearly states the tool's function with a specific verb ('Collect interactive feedback from users via a Web UI') and resource. It also describes the user interaction flow (review summary, type text, upload images). This distinguishes it from the only sibling tool, get_system_info, which serves a different purpose.

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

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context on when to use the tool (when collecting user feedback on AI work). It does not explicitly state exclusions or alternative tools, but since the sibling is unrelated, the guidance is sufficient. It could have been stronger with an explicit 'use this when' statement.

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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