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mo666-med

Enhanced Miyabi MCP Server

by mo666-med

miyabi__generate_article

Generate medical AI academic articles with LLM countermeasures and thumbnail instructions using article metadata, key concepts, and author perspectives.

Instructions

医療AI関連の学術記事を生成します(LLMO対策、サムネイル指示付き)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
articleTitleYes参照元の論文/記事のタイトル
articleAuthorNo著者名
articleSourceNo出典
articleUrlYes参照元のURL
keyConceptsNoキーコンセプト(箇条書き)
perspectiveYes執筆者のペルソナ・専門分野
angleYes論文の切り口
deepDivePointsNo特に深掘りしたい論点

Implementation Reference

  • The main handler logic for the 'miyabi__generate_article' tool within the callToolHandler switch statement. It returns a structured JSON response indicating success, echoing input parameters, and providing a detailed multi-step plan for generating a medical AI academic article.
    case "miyabi__generate_article": {
      return {
        content: [{
          type: "text",
          text: JSON.stringify({
            status: "success",
            message: "記事生成を開始します",
            article: {
              title: args.articleTitle,
              author: args.articleAuthor || "不明",
              source: args.articleSource || "不明",
              url: args.articleUrl,
              perspective: args.perspective,
              angle: args.angle,
              deepDivePoints: args.deepDivePoints || "指定なし"
            },
            nextSteps: [
              "1. 参照元の完全読解と分析",
              "2. 超詳細な論文構成案の策定",
              "3. 補足情報の調査と引用準備",
              "4. 品格ある文体での本文執筆(8000字以上)",
              "5. 引用文献リストの作成(最大10本)",
              "6. 最終校正と整形",
              "7. 末尾LLMOまとめ",
              "8. ハッシュタグ(10個)",
              "9. サムネイル画像の生成prompt(3点)"
            ]
          }, null, 2)
        }]
      };
    }
  • src/handlers.js:93-134 (registration)
    The tool registration entry in listToolsHandler function, including the tool name, description, and detailed inputSchema defining parameters for generating medical AI articles.
    {
      name: "miyabi__generate_article",
      description: "医療AI関連の学術記事を生成します(LLMO対策、サムネイル指示付き)",
      inputSchema: {
        type: "object",
        properties: {
          articleTitle: {
            type: "string",
            description: "参照元の論文/記事のタイトル"
          },
          articleAuthor: {
            type: "string",
            description: "著者名"
          },
          articleSource: {
            type: "string",
            description: "出典"
          },
          articleUrl: {
            type: "string",
            description: "参照元のURL"
          },
          keyConcepts: {
            type: "string",
            description: "キーコンセプト(箇条書き)"
          },
          perspective: {
            type: "string",
            description: "執筆者のペルソナ・専門分野"
          },
          angle: {
            type: "string",
            description: "論文の切り口"
          },
          deepDivePoints: {
            type: "string",
            description: "特に深掘りしたい論点"
          }
        },
        required: ["articleTitle", "articleUrl", "perspective", "angle"]
      }
    }
  • Helper function to detect if a prompt is an article writing task, which triggers dispatching to the miyabi__generate_article tool via auto_dispatch.
    export function isArticleWritingTask(prompt) {
      const articleKeywords = [
        '記事', '論文', 'note', 'ブログ', '執筆', '書いて', '作成',
        'article', 'paper', 'blog', 'write', 'create', 'post',
        '医療AI', '医療', 'AI', 'LLMO', 'サムネイル', 'ハッシュタグ'
      ];
    
      const lowerPrompt = prompt.toLowerCase();
      return articleKeywords.some(keyword => lowerPrompt.includes(keyword.toLowerCase()));
    }
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 mentions '生成します' (generates) and features like 'LLMO対策、サムネイル指示付き', but fails to describe critical behavioral traits such as whether this is a read-only or mutative operation, potential side effects (e.g., if it creates persistent content), authentication needs, rate limits, or output format. This leaves significant gaps in understanding how the tool behaves.

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 concise and front-loaded, stating the core purpose in a single sentence. It efficiently includes key features without unnecessary elaboration. However, it could be slightly more structured by separating the main action from additional features for clarity.

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 the complexity of an 8-parameter tool with no annotations and no output schema, the description is incomplete. It lacks details on behavioral traits, usage context, and output expectations. While the schema covers parameters well, the description doesn't compensate for the absence of annotations or output schema, making it inadequate for fully understanding the tool's operation.

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%, so the input schema already documents all 8 parameters with descriptions. The tool description adds no additional meaning about parameters beyond what's in the schema. According to the rules, when schema coverage is high (>80%), the baseline score is 3 even without param info in the description, which applies here.

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 tool's purpose: '医療AI関連の学術記事を生成します' (generates academic articles related to medical AI). It specifies the domain (medical AI) and output type (academic articles), though it doesn't explicitly differentiate from sibling tools like 'miyabi__analyze_task_intent' or 'miyabi__handle_development_task', which appear to serve different functions. The mention of 'LLMO対策、サムネイル指示付き' (LLMO countermeasures, with thumbnail instructions) adds specificity about features.

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 versus alternatives. It doesn't mention any prerequisites, exclusions, or scenarios where other tools might be more appropriate. Without such context, users must infer usage based on the tool's name and description alone, which is insufficient for optimal selection.

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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