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TeXmeijin

Manalink MCP Server

by TeXmeijin

get_course_features

Retrieve course feature details from the Manalink MCP Server to map labels and IDs for tutoring search criteria.

Instructions

コース特徴マスタを取得します。このマスタの取得を通じて、特徴のラベルとIDの対応を取得します。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • Full tool handler implementation including registration: calls fetchCourseFeatures and returns JSON response or error.
    server.tool(
      "get_course_features",
      "コース特徴マスタを取得します。このマスタの取得を通じて、特徴のラベルとIDの対応を取得します。",
      async () => {
        try {
          const features = await fetchCourseFeatures();
          return {
            content: [{
              type: "text" as const,
              text: JSON.stringify(features, null, 2)
            }]
          };
        } catch (error) {
          console.error('特徴マスタの取得に失敗しました:', error);
          return {
            content: [{
              type: "text" as const,
              text: `エラーが発生しました: ${error instanceof Error ? error.message : '不明なエラー'}`
            }],
            isError: true
          };
        }
      }
    );
  • TypeScript interface defining the structure of course features returned by the tool.
    export interface CourseFeature {
      id: number;
      name: string;
      description?: string;
    }
  • Async helper function that provides the course features data from hardcoded constant.
    export async function fetchCourseFeatures(): Promise<CourseFeature[]> {
      return COURSE_FEATURES;
    }
  • Hardcoded array of course features master data used by fetchCourseFeatures.
    const COURSE_FEATURES: CourseFeature[] = [
      { id: 1, name: "中学受験" },
      { id: 2, name: "高校受験" },
      { id: 3, name: "TOEIC" },
      { id: 5, name: "大学受験" },
      { id: 6, name: "発達障害" },
      { id: 8, name: "英検" },
      { id: 9, name: "定期テスト" },
      { id: 12, name: "共通テスト" },
      { id: 21, name: "中高一貫" },
      { id: 25, name: "看護" },
      { id: 27, name: "帰国子女" },
      { id: 32, name: "海外子女" },
      { id: 52, name: "医学部" },
      { id: 66, name: "不登校" },
      { id: 67, name: "平均点以下" },
      { id: 71, name: "公務員試験" },
      { id: 78, name: "小論文" }
    ];
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 retrieves a master list for label-ID correspondence, implying a read-only operation, but does not disclose other behavioral traits such as authentication requirements, rate limits, error conditions, or whether the data is cached or real-time. The description is minimal and misses key operational details.

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, consisting of two sentences that directly state the tool's purpose and outcome. There is no wasted text, and it efficiently communicates the essential information without redundancy or fluff.

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 0 parameters, no annotations, and no output schema, the description is minimally complete for a simple retrieval tool. It explains what is retrieved (course feature master for label-ID mapping) but lacks details on output format, error handling, or integration context. For a tool with no structured metadata, it meets basic needs but could be more informative.

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 input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description does not add parameter details beyond the schema, but since there are no parameters, a baseline score of 4 is appropriate as it adequately addresses the tool's function without unnecessary complexity.

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: 'コース特徴マスタを取得します' (retrieves course feature master) and explains the outcome: '特徴のラベルとIDの対応を取得します' (obtains correspondence between feature labels and IDs). It specifies the resource (course feature master) and the verb (retrieve), though it doesn't explicitly differentiate from sibling tools like get_grade_master or get_subject_master, which appear to be similar retrieval operations for different resources.

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 does not mention sibling tools (e.g., get_grade_master, get_subject_master, search_teachers_advanced) or specify contexts where this tool is appropriate, such as needing label-ID mappings for course features. Usage is implied by the purpose but lacks explicit instructions 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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