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., "@Word Cloud MCP create a word cloud from my report.pdf with the business theme"
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.
一个专注于从文档内容制作词云图的 MCP (Model Context Protocol) 工具，支持 PDF、Word、TXT、MD 等多种格式的智能文字提取。
PDF 文档：支持从 PDF 文件中提取文字内容
Word 文档：支持 .docx 和 .doc 格式的文档解析
文本文件：支持 .txt 纯文本文件
Markdown：支持 .md 和 .markdown 文件，自动清理 Markdown 语法
自动去除无意义的停用词（如"我"、"我们"、"的"、"了"等）
清理标点符号和特殊字符
智能分词处理，支持中英文混合文本
可自定义停用词列表
多种输出格式：SVG、PNG、JPG、WebP 等多种格式支持
多种主题：default、warm、cool、nature、business
灵活配置：字体大小、文字间隙、角度范围、背景色等
智能布局：避免文字重叠，优化视觉效果
高质量输出：支持高分辨率和质量调节
Related MCP server: docxtpl MCP Server
npm install -g @lucianaib/word-cloud-mcp在你的 MCP 客户端配置中添加此服务器：
方式一：使用 npx（推荐，适用于全局安装）
{
"mcpServers": {
"word-cloud": {
"command": "npx",
"args": ["@lucianaib/word-cloud-mcp"]
}
}
}
方式二：使用 node 直接运行（适用于本地开发）
{
"mcpServers": {
"word-cloud": {
"command": "node",
"args": ["path/to/word-cloud-mcp/dist/index.js"],
"cwd": "path/to/word-cloud-mcp"
}
}
}方式三：使用绝对路径（Windows 示例）
{
"mcpServers": {
"word-cloud": {
"command": "node",
"args": ["D:/word-cloud-mcp/dist/index.js"],
"cwd": "D:/word-cloud-mcp"
}
}
}重启你的 MCP 客户端（如 CodeBuddy、Cursor 等）
从文档文件中提取文字内容
参数：
filePath (string): 文档文件的路径
fileType (string): 文件类型 ('pdf' | 'docx' | 'txt' | 'md')
示例：
{
"filePath": "./documents/sample.pdf",
"fileType": "pdf"
}根据文字内容生成词云图
使用示例：
用MCP把下面的内容转换为词云图：Google AI Studio 和 Gemini API 的适用区域
content_copy
如果您在尝试打开 Google AI Studio 后进入此页面，可能是因为 Google AI Studio 在您所在的地区不可用，或者您未达到访问年龄要求（年满 18 周岁）。如需详细了解可用地区，请参阅下文；如需详细了解其他要求，请参阅服务条款。
可用区域
注意： 对于 Colab 用户 - 地区限制是根据 Colab 实例所在的地区应用，而不是根据用户所在的地区应用。您可以使用 !curl ipinfo.io
检查 Colab 实例的位置
Gemini API 和 Google AI Studio 已在以下国家和地区推出。如果您不在上述国家或地区，请尝试使用 Vertex AI 中的 Gemini API：
阿尔巴尼亚
阿尔及利亚
美属萨摩亚
安哥拉
....
参数：
text (string): 用于生成词云图的文字内容
theme (string, 可选): 主题色彩 (default: 'default')
shape (string, 可选): 词云图形状 (default: 'rectangle')
wordGap (number, 可选): 文字间隙 (default: 2)
fontSize (object, 可选): 文字大小范围 (default: {min: 10, max: 100})
angleRange (object, 可选): 角度范围 (default: {min: -90, max: 90})
angleStep (number, 可选): 角度步长 (default: 45)
outputPath (string, 可选): 输出文件路径 (default: './wordcloud.svg')
format (string, 可选): 输出格式 ('svg' | 'png' | 'jpg' | 'jpeg' | 'webp', default: 'svg')
backgroundColor (string, 可选): 背景颜色 (default: '#ffffff')
quality (number, 可选): JPG/WebP 格式的质量设置 (1-100, default: 90)
示例：
{
"text": "这是一段用于生成词云图的示例文字内容",
"theme": "warm",
"format": "png",
"fontSize": {"min": 15, "max": 80},
"backgroundColor": "#f8f9fa",
"outputPath": "./my-wordcloud.png"
}从文档文件直接生成词云图（组合操作）
参数：
filePath (string): 文档文件的路径
fileType (string): 文件类型
其他参数同 generate_wordcloud
示例：
{
"filePath": "./documents/article.md",
"fileType": "md",
"theme": "nature",
"outputPath": "./article-wordcloud.svg"
}经典彩色主题，适合大多数场景
暖色调主题，营造温馨氛围
冷色调主题，现代简约风格
自然色彩主题，清新自然
商务色彩主题，专业正式
格式 | 扩展名 | 说明 |
支持文字型 PDF，不支持扫描版 | ||
Word | .docx, .doc | Microsoft Word 文档 |
文本 | .txt | 纯文本文件 |
Markdown | .md, .markdown | Markdown 格式文档 |
格式 | 扩展名 | 特点 | 适用场景 |
SVG | .svg | 矢量图形，无损缩放，文件小 | 网页展示、印刷品、需要缩放的场景 |
PNG | .png | 支持透明背景，无损压缩 | 网页、演示文稿、需要透明背景 |
JPG | .jpg/.jpeg | 有损压缩，文件小，不支持透明 | 照片处理、社交媒体分享 |
WebP | .webp | 现代格式，压缩率高，质量好 | 现代网页、移动应用 |
# 克隆项目
git clone https://github.com/lfrbmw/word-cloud-mcp.git
cd word-cloud-mcp
# 安装依赖
npm install
# 构建项目
npm run build
# 运行测试
npm testsrc/
├── index.ts # MCP 服务器主入口
├── extractors/
│ └── text-extractor.ts # 文字提取器
├── utils/
│ └── content-cleaner.ts # 内容清理器
└── wordcloud/
└── generator.ts # 词云图生成器MIT License
欢迎提交 Issue 和 Pull Request！
从文档文件直接生成词云图（提取文字 + 生成词云图的组合操作）
| Name | Required | Description | Default |
|---|---|---|---|
| filePath | Yes | 文档文件的路径 | |
| fileType | Yes | 文件类型 | |
| theme | No | 词云图主题色彩 | default |
| shape | No | 词云图形状 | rectangle |
| wordGap | No | 文字间隙 | |
| fontSize | No | 文字大小范围 | |
| angleRange | No | 角度范围 | |
| angleStep | No | 角度步长 | |
| outputPath | No | 输出文件路径（支持绝对路径和相对路径） | ./wordcloud.svg |
| format | No | 输出格式（svg、png、jpg、jpeg、webp） | svg |
| backgroundColor | No | 背景颜色（十六进制颜色代码） | #ffffff |
| quality | No | JPG/WEBP 格式的质量设置（1-100） |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions this is a '组合操作' (combined operation) but doesn't specify whether this creates files, modifies data, requires specific permissions, has rate limits, or what the output looks like. For a tool with 12 parameters that presumably generates files, this is insufficient behavioral context. The description doesn't contradict annotations (none exist), but fails to provide needed behavioral information.
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 Chinese sentence that clearly states the core functionality. It's appropriately concise without being under-specified. The structure is front-loaded with the main purpose. While it could potentially include more context, what's present is well-structured and wastes no words.
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 complexity (12 parameters, nested objects, no output schema, and no annotations), the description is incomplete. It doesn't explain what the tool returns (presumably a file path or image data), doesn't mention error conditions, and provides no behavioral context for a tool that likely creates files. With rich input schema but no output schema and no annotations, the description should do more to help the agent understand the complete operation.
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?
Schema description coverage is 100%, so the schema already documents all 12 parameters thoroughly. The description adds no parameter-specific information beyond the general concept of generating word clouds from files. It doesn't explain parameter relationships, constraints, or provide additional semantic context. With complete schema coverage, the baseline is 3 even without parameter details in the 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?
The description clearly states the tool's purpose: '从文档文件直接生成词云图' (generate word cloud directly from document files) and specifies it's a combination operation of text extraction and word cloud generation. It distinguishes from sibling tools by mentioning this combined functionality, though it doesn't explicitly name the alternatives. The purpose is specific (verb+resource+scope) but could be more explicit about differentiation.
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 the sibling tools (extract_text_from_file and generate_wordcloud). It doesn't mention prerequisites, alternatives, or specific contexts where this combined operation is preferable to using the separate tools. The agent receives no usage guidance beyond the basic functionality description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
从文档文件中提取文字内容，支持 PDF、Word、TXT、MD 等格式
| Name | Required | Description | Default |
|---|---|---|---|
| filePath | Yes | 文档文件的路径 | |
| fileType | Yes | 文件类型 |
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 what the tool does but lacks details on behavioral traits like error handling (e.g., for unsupported formats or corrupted files), performance (e.g., speed or size limits), or output specifics (e.g., text encoding or formatting). This is a significant gap for a tool with no annotation coverage.
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 that clearly states the tool's purpose and supported formats without any wasted words. It is appropriately sized and front-loaded, making it easy to understand at a glance.
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 complexity of a file processing tool with no annotations and no output schema, the description is incomplete. It lacks information on output behavior (e.g., what the extracted text looks like, error messages), performance constraints, or usage context. This makes it inadequate for an agent to fully understand how to invoke and interpret results from this tool.
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 the schema already documents both parameters ('filePath' and 'fileType') with descriptions and an enum for 'fileType'. The description adds minimal value by listing the supported formats, which aligns with the enum, but doesn't provide additional syntax or usage details beyond what the schema provides. Baseline 3 is appropriate when the schema does the heavy lifting.
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: extracting text content from document files, with specific formats listed (PDF, Word, TXT, MD). It uses a specific verb ('extract') and resource ('text content from document files'), but doesn't explicitly differentiate from sibling tools like 'create_wordcloud_from_file' or 'generate_wordcloud', which appear to be different operations.
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 lists supported formats but doesn't mention when to choose this tool over sibling tools (e.g., for text extraction vs. word cloud generation) or any prerequisites, such as file accessibility or format limitations beyond the listed ones.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
根据文字内容生成词云图
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | 用于生成词云图的文字内容 | |
| theme | No | 词云图主题色彩 | default |
| shape | No | 词云图形状 | rectangle |
| wordGap | No | 文字间隙 | |
| fontSize | No | 文字大小范围 | |
| angleRange | No | 角度范围 | |
| angleStep | No | 角度步长 | |
| outputPath | No | 输出文件路径（支持绝对路径和相对路径） | ./wordcloud.svg |
| format | No | 输出格式（svg、png、jpg、jpeg、webp） | svg |
| backgroundColor | No | 背景颜色（十六进制颜色代码） | #ffffff |
| quality | No | JPG/WEBP 格式的质量设置（1-100） |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states the tool generates a word cloud image but doesn't disclose important behavioral aspects: whether this is a read-only operation, what happens to the output file, whether there are rate limits, authentication requirements, or performance characteristics. For a tool with 11 parameters and file output, this is a significant gap.
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 extremely concise - a single Chinese sentence that directly states the tool's purpose. There's no wasted language or unnecessary elaboration. It's front-loaded with the core functionality and doesn't include any extraneous information.
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 tool with 11 parameters, no annotations, and no output schema, the description is inadequate. It doesn't explain what the tool returns (presumably a file path or image data), error conditions, performance expectations, or how the various parameters interact. The single-sentence description fails to provide sufficient context for an AI agent to use this tool effectively despite its complexity.
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?
Schema description coverage is 100%, so the schema already documents all 11 parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema. It mentions '文字内容' (text content) which aligns with the 'text' parameter, but provides no additional context about parameter interactions or usage patterns.
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: '根据文字内容生成词云图' (Generate a word cloud image based on text content). It specifies the verb ('生成' - generate) and resource ('词云图' - word cloud image). However, it doesn't differentiate from sibling tools like 'create_wordcloud_from_file' which likely generates word clouds from files rather than direct text input.
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. There are sibling tools ('create_wordcloud_from_file' and 'extract_text_from_file') that likely serve related purposes, but the description doesn't mention them or explain when this direct text input tool is preferable over file-based alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v3.0.0create_wordcloud_from_fileextract_text_from_filegenerate_wordcloudThe tools have significant overlap and unclear boundaries. 'create_wordcloud_from_file' combines extraction and generation, while 'extract_text_from_file' and 'generate_wordcloud' are its components, making them redundant and confusing for an agent to choose between. This overlap creates ambiguity about when to use the combined tool versus the separate ones.
All tool names follow a consistent snake_case pattern with clear verb_noun structure (e.g., create_wordcloud_from_file, extract_text_from_file, generate_wordcloud). The naming is predictable and readable across the set, with no deviations in style or convention.
With 3 tools, the count is borderline thin for a word cloud domain, as it might lack advanced features like customization or analysis. However, it covers basic operations, so it's not severely mismatched but feels minimal and could benefit from additional tools for a more complete workflow.
The tools cover core word cloud creation from files and text, but there are notable gaps. Missing operations include customizing word cloud parameters (e.g., colors, shapes), saving/output options, or analyzing word frequencies. The surface allows basic generation but lacks flexibility for more complex agent tasks.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Generate word clouds from text with custom fonts, colors, backgrounds, gradients, and shape masks
Generate and vectorize clean, editable SVG graphics from text, images, or both.
Parse PDF/Word/PPT/HTML to Markdown; tables as JSON, image extraction, RAG chunking, page ranges.
Convert documents and web pages to clean Markdown: PDF, DOCX, XLSX, EPUB, scanned files, any URL.
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/OnePieceLwc/word-cloud-mcp'
If you have feedback or need assistance with the MCP directory API, please join our Discord server
