Xmind Generator MCP Server
Xmind Generator MCP 서버
Xmind 마인드맵을 생성하기 위한 MCP(Model Context Protocol) 서버입니다. 이 서버를 통해 LLM은 MCP 프로토콜을 통해 구조화된 마인드맵을 생성할 수 있습니다.
특징
계층적 주제 구조를 사용하여 Xmind 마인드 맵 생성
주제 메모, 레이블 및 마커 지원
마인드맵을 로컬 파일에 저장
Claude Desktop 및 기타 MCP 클라이언트와의 간편한 통합
Related MCP server: MCP Thinking Server
필수 조건
Node.js : 버전 18 이상이 필요합니다.
Xmind : 생성된 마인드 맵을 열고 편집하려면 Xmind 데스크톱 애플리케이션을 설치하세요.
Claude Desktop : 이 도구를 확장 프로그램으로 사용하려면 필요합니다.
Claude Desktop으로 설정
옵션 1: npx 사용(권장)
Claude Desktop 구성 파일을 생성하거나 편집합니다.
macOS:
~/Library/Application Support/Claude/claude_desktop_config.jsonWindows:
%APPDATA%\Claude\claude_desktop_config.json
다음 구성을 추가합니다.
지엑스피1
Claude Desktop을 다시 시작하세요
대화에서 Xmind 생성기를 사용해 보세요
옵션 2: 로컬 설치
저장소를 복제합니다.
git clone https://github.com/BangyiZhang/xmind-generator-mcp.git cd xmind-generator-mcp npm install npm run buildClaude Desktop 구성 파일을 생성하거나 편집합니다.
macOS:
~/Library/Application Support/Claude/claude_desktop_config.jsonWindows:
%APPDATA%\Claude\claude_desktop_config.json
다음 구성을 추가합니다.
{ "mcpServers": { "xmind-generator": { "command": "node", "args": ["path/to/xmind-generator-mcp/dist/index.js"], "env": { "outputPath": "/path/to/save/xmind/files", "autoOpenFile": "false" } } } }path/to/xmind-generator-mcp복제된 프로젝트의 실제 경로로 바꾸세요.Claude Desktop을 다시 시작하세요
대화에서 Xmind 생성기를 사용해 보세요
참고 : env 섹션은 선택 사항입니다. 이 섹션을 통해 서버의 환경 변수를 설정할 수 있습니다.
outputPath: Xmind 파일이 저장되는 기본 디렉터리 또는 파일 경로입니다. 도구 호출 시outputPath매개변수로 재정의할 수 있습니다.autoOpenFile: 생성된 Xmind 파일을 생성 후 자동으로 열지 여부를 제어합니다. 자동 열기를 비활성화하려면 "false"로 설정합니다(기본값은 "true").
사용 가능한 도구
마인드맵 생성
주제의 계층 구조에서 Xmind 마인드 맵을 생성합니다.
매개변수:
title(문자열): 마인드맵의 제목(루트 주제)topics(배열): 마인드 맵에 포함할 주제 배열title(문자열): 주제의 제목ref(문자열, 선택 사항): 주제에 대한 참조 IDnote(문자열, 선택 사항): 주제에 대한 참고 사항labels(문자열 배열, 선택 사항): 주제에 대한 레이블markers(문자열 배열, 선택 사항): 주제에 대한 마커(형식: "Category.name", 예: "Arrow.refresh")children(배열, 선택 사항): 자식 주제의 배열
relationships(배열, 선택 사항): 주제 간 관계 배열outputPath(문자열, 선택 사항): Xmind 파일의 사용자 지정 출력 경로입니다. 이 경로가 설정된 경우 환경 변수보다 우선합니다.
예
다음은 generate-mind-map 도구를 사용하는 방법의 예입니다.
{
"title": "Project Plan",
"topics": [
{
"title": "Research",
"ref": "topic:research",
"note": "Gather information about the market",
"children": [
{
"title": "Market Analysis",
"labels": ["Priority: High"]
},
{
"title": "Competitor Research",
"markers": ["Task.quarter"]
}
]
},
{
"title": "Development",
"children": [
{
"title": "Frontend",
"markers": ["Arrow.refresh"]
},
{
"title": "Backend"
}
]
}
]
}특허
MIT
Available Tools
1 toolgenerate-mind-mapD
| Name | Required | Description | Default |
|---|---|---|---|
| filename | Yes | The filename for the XMind file (without path or extension) | |
| outputPath | No | Optional custom output path for the XMind file. If not provided, the file will be created in the temporary directory. | |
| relationships | No | Optional array of relationships between topics | |
| title | Yes | The title of the mind map (root topic) | |
| topics | Yes | Array of topics to include in the mind map |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Tool has no description.
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?
Tool has no description.
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?
Tool has no description.
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?
Tool has no description.
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?
Tool has no description.
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?
Tool has no description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
1 tool update
v1.0.0- First observed
generate-mind-map
TDQS
Scored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool 'generate-mind-map' stands alone with a distinct purpose, making it impossible for an agent to misselect between multiple options.
The naming is trivially consistent as there is only one tool. It follows a clear verb_noun pattern ('generate-mind-map'), and with no other tools to compare against, there is no inconsistency in naming conventions.
A single tool is generally too few for a server's purpose, as it limits functionality and may not cover the domain adequately. For a mind map generator, one tool might suffice for basic generation, but it feels thin and could lack features like editing, saving, or retrieving maps, suggesting an under-scoped implementation.
The tool surface is severely incomplete for a mind map domain. With only a generation tool, there are obvious gaps such as no ability to retrieve, update, delete, or list existing mind maps. This will likely cause agent failures when trying to perform full lifecycle operations, as the server does not support basic CRUD functionality.
Maintenance
Related MCP Connectors
Turn outlines and hierarchical notes into interactive mind maps through a hosted remote MCP server.
MCP server for generating rough-draft project plans from natural-language prompts.
MCP server for AI dialogue using various LLM models via AceDataCloud
MCP server for building and testing AI agents with multi-model experimentation and insights.
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