A focused Markdown formatter for WeChat Official Accounts and Zhihu with MCP server capabilities for markdown processing
easymd provides 4 markdown-focused tools with basic schemas and descriptions in Chinese. All tools have input schemas with maxLength constraints and descriptions, but lack critical production-grade details: no output schemas documented, no error handling guidance, no parameter validation rules beyond maxLength, and descriptions lack WHEN/WHY context. Tool names are action verbs (extract, lint, parse, render) which is good, but descriptions are generic and don't explain dependencies or failure modes. No tool annotations (readOnlyHint/destructiveHint) despite clear risk profiles. Schemas are minimal JSON Schema with only type and description, no enums, patterns, or structured output definitions. This is typical of early-stage community servers.
从 Markdown 文档中提取纯文本内容,移除所有格式标记(如标题符号、链接、图片、代码块等)。适用于字数统计、文本分析、生成摘要、或作为语音朗读的输入。
使用 markdownlint 规则对 Markdown 内容进行规范校验,并自动修复可修复的问题。包括:统一标题层级、修复列表缩进、规范空行、移除行尾空格等。
解析 Markdown 文档为抽象语法树(AST),用于深度分析文档结构、提取元数据、或进行高级文本处理。
将 Markdown 文档渲染为 HTML,支持 GFM 扩展、数学公式、代码高亮等功能。可用于生成网页内容、导出 HTML 文件或进行内容预览。
No output schemas documented. LLMs cannot plan downstream tool calls or extract structured data without knowing what fields to expect.
Descriptions lack WHEN/WHY context and are under 100 chars. E.g., 'extract' says what it does but not when to use it vs parse/render, or what format the output is.
No tool annotations (readOnlyHint/destructiveHint/idempotentHint). 'lint' modifies content (WRITE risk) but LLM has no signal that it's destructive.
Input schema only specifies maxLength; no validation rules, format constraints, or error guidance. LLMs cannot self-correct invalid input.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | D | 53 | 2025-06-18+ | v2 |
No error handling guidance. If markdown parsing fails or lint auto-fix breaks syntax, LLM has no recovery path.