Word Cloud MCP
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| extract_text_from_fileC | 从文档文件中提取文字内容,支持 PDF、Word、TXT、MD 等格式 |
| generate_wordcloudC | 根据文字内容生成词云图 |
| create_wordcloud_from_fileC | 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) |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 3 tools
The 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.