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zhiyingzzhou

AI Notify MCP

by zhiyingzzhou

auto_notify_completion

Automatically sends system notifications when AI completes responses in MCP-compatible code editors, with cross-platform support for macOS, Windows, and Linux.

Instructions

Automatically show completion notification (call this after providing any response)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
responseLengthNoLength of the response (optional)

Implementation Reference

  • The main handler logic for the 'auto_notify_completion' tool. It customizes the notification message based on the optional responseLength parameter and sends a system notification, then returns a confirmation message.
    case 'auto_notify_completion': {
      const responseLength = (args?.responseLength as number) || 0;
      let autoMessage = '已完成回答';
    
      if (responseLength > 0) {
        if (responseLength > 1000) {
          autoMessage = '已完成详细回答';
        } else if (responseLength > 500) {
          autoMessage = '已完成回答';
        } else {
          autoMessage = '已完成简短回答';
        }
      }
    
      await notificationService.notify({
        title: formatNotificationTitle('AI Notify', projectName),
        message: autoMessage,
        sound: true,
        icon: iconPath,
      });
    
      return {
        content: [
          {
            type: 'text',
            text: `🔔 自动通知已发送`,
          },
        ],
      };
    }
  • src/index.ts:164-178 (registration)
    The tool registration entry in the tools array, including name, description, and input schema. This is used by the MCP server to expose the tool.
    {
      name: 'auto_notify_completion',
      description:
        'Automatically show completion notification (call this after providing any response)',
      inputSchema: {
        type: 'object',
        properties: {
          responseLength: {
            type: 'number',
            description: 'Length of the response (optional)',
            default: 0,
          },
        },
      },
    },
  • Input schema definition for the 'auto_notify_completion' tool, specifying the optional responseLength parameter.
    inputSchema: {
      type: 'object',
      properties: {
        responseLength: {
          type: 'number',
          description: 'Length of the response (optional)',
          default: 0,
        },
      },
    },
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. It mentions the tool 'shows' a notification, implying a display action, but lacks details on behavioral traits such as what triggers the notification, if it's user-visible, whether it requires specific permissions, or if it has side effects like logging. This leaves significant gaps in understanding how the tool behaves beyond its basic function.

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

Conciseness4/5

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

The description is brief and to the point, consisting of one clear sentence that states the purpose and usage. It is appropriately sized without unnecessary words, though it could be slightly more structured by separating purpose from guidelines for better readability.

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

Completeness3/5

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

Given the tool has no annotations, no output schema, and a simple input schema, the description provides basic purpose and usage but lacks completeness. It doesn't cover what the notification looks like, how it's triggered, or any error conditions, which are important for a tool that interacts with user interfaces. The context is minimal but adequate for a simple tool.

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?

The input schema has 100% description coverage, with the parameter 'responseLength' documented as optional with a default. The description adds no additional meaning about parameters beyond what the schema provides, such as explaining why response length matters or how it affects the notification. Thus, it meets the baseline for high schema coverage but doesn't enhance parameter understanding.

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

Purpose3/5

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

The description states the action ('Automatically show completion notification') and provides a usage instruction ('call this after providing any response'), which clarifies the purpose. However, it doesn't specifically differentiate from the sibling tool 'show_completion_notification', leaving ambiguity about how they differ (e.g., 'automatically' vs. manual).

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 explicitly states when to use the tool ('after providing any response'), which provides clear context for its application. However, it doesn't mention when not to use it or how it differs from the sibling tool 'show_completion_notification', missing explicit alternatives or exclusions.

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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