Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Manalink MCP Server find math tutors for middle school students"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Manalink's Model Context Protocol (MCP) server implementation, allowing AI assistants to use features such as teacher search.
This MCP server provides the following tools:
Obtain subject master : Obtain master of subject name and subject ID
Get feature master : Get the master of course features (dynamically obtained from API)
Get grade master : Get master of grade name and ID
Teacher search (detailed) : Search for teachers by detailed parameters such as subject ID, grade ID, etc.
Related MCP server: OpenEdu MCP Server
Node.js 18+
npm or yarn
# リポジトリのクローン
git clone [リポジトリURL]
cd manalink-mcp
# 依存関係のインストール
npm installnpm run devnpm run build
npm startTo use it with Claude for Desktop, add the setting to the claude_desktop_config.json file.
Open the Claude for Desktop configuration file
code ~/Library/Application\ Support/Claude/claude_desktop_config.jsonAdd the following settings
{
"mcpServers": {
"manalink": {
"command": "node",
"args": [
"/絶対パス/manalink-mcp/dist/index.js"
]
}
}
}Restart Claude for Desktop
// ツール名: get_subject_master
// パラメータ: なし// ツール名: get_course_features
// パラメータ: なし// ツール名: get_grade_master
// パラメータ: なし// ツール名: search_teachers_advanced
// パラメータ:
// - subject_ids: 科目IDの配列（オプション）
// - grade_ids: 学年IDの配列（オプション）
// - course_feature_id: 特徴ID（オプション）
// - sort: ソート順（オプション）[pr, certification, rating, lesson_count, latest]
// - desired_teaching_period: 指導期間（オプション）[monthly, once]You can use MCP Inspector to verify.
Run the build and then run the inspector
npm run build
npx @modelcontextprotocol/inspector node dist/index.jsMIT
It implements a function to convert HTML content obtained from Manalink into Markdown format.
npm install node-html-markdownimport { convertHtmlToMarkdown } from './utils/api';
// HTMLからMarkdownへの変換
const html = `
<div>
<h1>マナリンク</h1>
<p>オンライン家庭教師プラットフォーム</p>
<ul>
<li><a href="/about">サービスについて</a></li>
<li><a href="/teachers">先生を探す</a></li>
</ul>
</div>
`;
const markdown = convertHtmlToMarkdown(html);
console.log(markdown);The above HTML will generate the following markdown:
# マナリンク
オンライン家庭教師プラットフォーム
* [サービスについて](/about)
* [先生を探す](/teachers)The Teacher Search API allows you to retrieve search results in both HTML and Markdown formats:
import { searchTeachers } from './utils/api';
// 数学の中学生を教えられる先生を検索
const result = await searchTeachers({
subject_ids: [2], // 数学
grade_ids: [4], // 中学1年生
sort: 'rating'
});
// HTML形式の検索結果
console.log(result.bodyContent);
// Markdown形式の検索結果
console.log(result.markdown);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。
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
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.
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.
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.
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.
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.
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.
学年マスタを取得します。このマスタの取得を通じて、学年のラベルとIDの対応を取得します。
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
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 states this is a retrieval operation ('取得します'), which implies read-only behavior, but doesn't address other important aspects like authentication requirements, rate limits, error conditions, or what happens when no data exists. For a tool with zero annotation coverage, this leaves significant behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately concise with two sentences that directly address the tool's function and purpose. The first sentence states the action, the second explains the value. There's no wasted language or unnecessary elaboration, though it could be slightly more structured for maximum clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (no parameters, no output schema, no annotations), the description is adequate but has clear gaps. It explains what the tool does and what information it provides, but doesn't address behavioral aspects like error handling or performance characteristics. For a read-only master data retrieval tool, this is minimally viable but could be more complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters with 100% schema description coverage, so the baseline is 4. The description appropriately doesn't discuss parameters since none exist, and it correctly focuses on what the tool returns (grade label-to-ID mappings) rather than input requirements.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: retrieving a 'grade master' (学年マスタ) to obtain mappings between grade labels and IDs. It uses specific verbs ('取得します' - retrieves/gets) and identifies the resource (grade master). However, it doesn't explicitly differentiate from sibling tools like get_subject_master, which appears to be a similar master data retrieval tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 prerequisites, appropriate contexts, or comparisons with sibling tools like get_subject_master or search_teachers_advanced. The only implied usage is when grade label-to-ID mappings are needed, but this is basic functional information rather than true usage guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
科目マスタを取得します。このマスタの取得を通じて、科目のラベルとIDの対応を取得します。
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
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 '取得します' (gets/retrieves), implying a read-only operation, but doesn't clarify aspects like authentication needs, rate limits, error conditions, or whether it returns all data or requires filtering. For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two concise sentences in Japanese, front-loaded with the core purpose and followed by a clarifying detail about the output. Every word earns its place, with no redundancy or unnecessary elaboration, making it efficient and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (0 parameters, no output schema, no annotations), the description is adequate but has clear gaps. It explains the purpose and output semantics, which is sufficient for basic use. However, it lacks behavioral context (e.g., read-only nature, potential constraints) and usage guidelines relative to siblings, making it minimally viable but not fully comprehensive.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
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 adds value by explaining the semantic output: '科目のラベルとIDの対応を取得します' (obtains correspondence between subject labels and IDs), which clarifies what the tool returns beyond just 'subject master'. This compensates for the lack of an output schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: '科目マスタを取得します' (get subject master) and specifies what is obtained: '科目のラベルとIDの対応' (correspondence between subject labels and IDs). It distinguishes the resource (subject master) and the specific mapping provided. However, it doesn't explicitly differentiate from sibling tools like get_grade_master or get_course_features, which likely retrieve different master data types.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 sibling tools (e.g., get_grade_master for grade data) or specify contexts where this tool is appropriate (e.g., when needing label-ID mappings for subjects). Usage is implied only by the purpose statement, with no explicit when/when-not instructions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
科目、学年、特徴、ソート順、指導期間を指定して先生を検索します
| Name | Required | Description | Default |
|---|---|---|---|
| course_feature_id | No | 特徴ID | |
| desired_teaching_period | No | 指導期間 (monthly: 長期, once: 短期) | |
| grade_ids | No | 学年IDの配列 | |
| sort | No | ソート順 | |
| subject_ids | No | 科目IDの配列 |
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 only states what parameters can be specified for searching, without mentioning whether this is a read-only operation, what permissions might be required, how results are returned (pagination, format), or any rate limits. For a search tool with 5 parameters and no annotation coverage, this is inadequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence in Japanese that directly states the tool's function with zero wasted words. It's appropriately sized and front-loaded with the core purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a search tool with 5 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain the result format, pagination, error conditions, or how empty/multiple parameters affect the search. The agent lacks sufficient context to use this tool effectively beyond basic parameter passing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, so all parameters are documented in the schema itself. The description lists the parameter categories (subject, grade, features, sort order, teaching period) but doesn't add meaningful semantic context beyond what the schema already provides, such as explaining how these filters interact or providing usage examples.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose as searching for teachers with specific criteria (subject, grade, features, sort order, teaching period), which is a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like get_course_features, get_grade_master, or get_subject_master, which appear to be metadata retrieval tools rather than teacher search tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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, constraints, or comparison with sibling tools, leaving the agent to infer usage context solely from the tool name and parameters.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Each tool has a clearly distinct purpose: three tools retrieve specific master data (course features, grade, subject), while the fourth performs an advanced search combining those elements. There is no overlap in functionality, making tool selection straightforward for an agent.
All tool names follow a consistent verb_noun pattern with 'get_' for retrieval operations and 'search_' for the search function. The naming is uniform and predictable across all four tools, enhancing readability and usability.
With 4 tools, the count is reasonable for a server focused on educational data management. It covers core master data retrieval and a search function, though it might feel slightly thin if more operations (e.g., updates or deletions) are expected in the domain.
The tools provide good coverage for retrieving master data and searching teachers, but there are notable gaps: no CRUD operations for creating, updating, or deleting records, which limits full lifecycle management. The search tool is comprehensive, but the set lacks broader operational capabilities.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
A Model Context Protocol server for Wix AI tools
A comprehensive Model Context Protocol (MCP) server that enables AI assistants to interact with yo…
Model Context Protocol server for Studex tools, notifications, and profile integrations
Capability registry for the agentic economy. Semantic search over verified MCP server listings.
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/TeXmeijin/manalinkMCP'
If you have feedback or need assistance with the MCP directory API, please join our Discord server