模型上下文协议 (MCP) 服务器，用于根据 5 种真实 ATS 解析器对简历进行 lint 检查。
提供三个 MCP 工具，以便任何兼容 MCP 的 AI 代理（Claude Code、Cursor、Windsurf、Zed、OpenCode、Cline 等）能够根据以下系统的已知特性分析简历：
Workday
Greenhouse
Lever
Taleo (Oracle)
iCIMS
这些是使用最广泛的 5 种企业级 ATS 系统。每种系统解析简历的方式略有不同。“0-100 ATS 分数”将这些差异平均化为噪音。此服务器将每种系统实际的操作方式（列处理、表情符号剥离、页眉页脚丢弃、日期格式怪癖）作为具体的 lint 发现结果和修复建议呈现出来。
lint 规则源自供应商的公开文档。引用请参阅 docs/vendor-sources.md。
Related MCP server: decroche-mcp
工具 | 描述 |
| 完整报告。接收简历文件路径（PDF 或 DOCX），返回各供应商的 lint 发现结果，包含严重程度（ |
| 单一供应商 lint。接收路径 + 供应商名称。当用户只关心某一个 ATS 时速度更快。 |
| 返回可视化 Web 配套工具 (cv-mirror-web.vercel.app) 的 URL，供喜欢在浏览器中查看并排解析视图而非代理会话的用户使用。 |
npm install -g cv-mirror-mcp添加到您的 MCP 客户端配置文件中：
Claude Code (~/.claude/mcp.json 或项目中的 .claude.json)：
{
"mcpServers": {
"cv-mirror": {
"command": "npx",
"args": ["-y", "cv-mirror-mcp"]
}
}
}Cursor / Windsurf / Zed / Cline： 相同的配置格式（粘贴到您的客户端读取 mcpServers 的位置）。
重启客户端。上述三个工具会自动注册。
安装完成后，只需询问您的代理：
扫描我位于
~/Documents/resume.pdf的简历，并告诉我每个 ATS 会如何处理它。
代理调用 analyze_cv，lint 引擎在本地运行（无网络，无上传），代理呈现结构化输出：
CV Mirror — multi-vendor ATS lint report
Source: /Users/jane/Documents/resume.pdf
Format: pdf
Pages: 2
Words: 542
[ERROR] Workday
- ERROR WORKDAY_MULTI_COLUMN: 35% of lines look multi-column. Workday's parser
reads left-to-right and interleaves both columns into one stream.
Fix: Convert to single-column layout. Move sidebars (Skills, Tools, Languages)
above or below the main content.
[OK] Greenhouse
No issues detected by the simulated parser.
[WARN] Lever
- WARN LEVER_HEADER_FOOTER: Header/footer-like text detected ("Page 1 of 2").
Lever historically drops content placed in PDF headers/footers.
Fix: Remove headers and footers. Page numbers are not needed on a CV.
...这些是源自公开 ATS 文档和供应商支持文章的真实启发式规则。包含引用的完整列表位于 docs/vendor-sources.md。
规则 | 供应商 | 严重程度 | 触发条件 |
| Workday | error | >15% 的行有 5 个以上空格的间隙 |
| Workday | warn | 日期使用 "Q3 2024" 格式 |
| Workday | error | 纯文本中没有电子邮件或电话 |
| Greenhouse | warn | 检测到任何表情符号代码点 |
| Greenhouse | info | 非标准项目符号字形 |
| Greenhouse | warn | "My Story", "Highlights Reel" 等 |
| Lever | warn | 检测到 "Page X of Y" 模式 |
| Lever | error | 没有 "Experience"/"Education" 部分 |
| Taleo | warn | ISO 日期多于月-年日期 |
| Taleo | error | 每 kB 文件大小少于 1 个单词 |
| Taleo | info | 检测到弯引号 |
| iCIMS | error | >20% 的行是多列 |
| iCIMS | warn | 找到少于 2 个标准标题 |
Node.js >= 18
兼容 MCP 的客户端 (Claude Code, Cursor, Windsurf, Zed, Cline 等)
服务器在您的代理进程内本地运行。没有上传端点。没有遥测。您的简历字节永远不会离开您的机器。
cv-mirror-web.vercel.app 上的可视化 Web 配套工具也完全在客户端运行——使用相同的引擎，在浏览器中运行。
git clone https://github.com/goofypluto999/cv-mirror-mcp.git
cd cv-mirror-mcp
npm install
npm test19 个单元测试，涵盖信号提取和各供应商规则触发。
CV Mirror (web) — 可视化并排解析视图。相同的 lint 引擎。拖放 PDF，在实际文档上查看阅读顺序叠加层。
Vantage AI — 一旦您的简历通过了解析器，下一个问题就是申请：定制求职信、模拟面试、匹配度分析。Vantage 处理该流程。付费（£5 入门 / 20 个代币，注册赠送 10 个免费代币）。
欢迎提交 Pull Request。特别感兴趣的是：
更多的供应商模拟器 (BambooHR, SmartRecruiters, JazzHR, Recruitee 等)
当供应商更改其解析行为时更新规则（请附带来源链接提交 issue）
lint 输出的翻译
MIT。Workday、Greenhouse、Lever、Taleo 和 iCIMS 是其各自所有者的商标。本项目与他们中的任何一个均无关联；名称仅用于描述性参考（提名合理使用）。
由 Vantage Labs 构建。
Analyse a CV (PDF or DOCX) against 5 real ATS parsers (Workday, Greenhouse, Lever, Taleo, iCIMS). Returns per-vendor lint findings, parse risk score, and concrete fixes. Use when the user asks 'is my CV ATS-friendly', 'will my resume pass [vendor]', or 'why am I not getting interviews' (with a file path).
| Name | Required | Description | Default |
|---|---|---|---|
| path | Yes | Absolute path to the CV file (PDF or DOCX). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations, so description carries full burden. It discloses tool reads PDF/DOCX, runs against 5 parsers, and returns findings. Does not mention file size limits, processing duration, or if file is uploaded elsewhere, but is largely transparent about its operation.
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?
Two focused sentences: first defines action and output, second provides usage examples. No unnecessary words. Excellent front-loading of 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?
Despite no output schema, description explains return types (per-vendor lint findings, risk score, fixes). Mentions supported file types. Could add error handling details (e.g., missing file), but otherwise complete for a single-parameter 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?
Input schema has 100% coverage with description for the single 'path' parameter. The description does not add further details beyond schema, but schema itself is sufficient. Baseline 3 applies.
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 explicitly states the tool analyzes CVs against 5 ATS parsers, returning per-vendor lint findings, risk score, and fixes. It clearly distinguishes from siblings by covering multiple vendors (vs. lint_for_vendor which likely targets one).
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?
Description provides explicit user query triggers ('is my CV ATS-friendly', 'will my resume pass [vendor]', 'why am I not getting interviews') and mentions file path requirement. Lacks explicit when-not-to-use or mention of sibling alternatives, but context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Returns the URL for the visual web companion tool (CV Mirror) where the user can drag-drop a CV and see the side-by-side parser view in their browser. Useful when the user wants the visual reading-order overlay or doesn't want to share a file path.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. States it returns a URL, implying read-only, but does not explicitly declare non-destructive behavior or other constraints.
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?
Two sentences, no unnecessary words, front-loaded with purpose. Highly efficient.
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?
Adequate for a zero-parameter, no-output-schema tool. Explains function and usage context. Minor gap: doesn't explicitly state no input needed, but schema implies it.
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?
No parameters (schema coverage 100%), baseline 4. Description adds meaning by explaining the purpose of the URL beyond the empty 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?
Clear verb+resource: 'Returns the URL' for a specific visual tool (CV Mirror). Distinguishes from siblings (analyze_cv, lint_for_vendor) by offering a different capability.
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?
States when it's useful (visual reading-order overlay, avoiding file path sharing). Does not explicitly exclude alternative uses but context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Run lint for ONE specific ATS vendor only. Use when the user asks something vendor-specific like 'will my CV pass Workday' or 'what would Greenhouse strip from this'. Vendor must be one of: workday, greenhouse, lever, taleo, icims.
| Name | Required | Description | Default |
|---|---|---|---|
| path | Yes | Absolute path to the CV file (PDF or DOCX). | |
| vendor | Yes | ATS vendor to simulate. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It does not explain whether the tool is read-only, what side effects exist, or what the output format is. The term 'lint' implies analysis but lacks detail.
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?
Two sentences, front-loaded with the core action and constraint. Each sentence adds value: one states the purpose and allowed vendors, the other gives usage examples. No wasted 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?
Despite its simplicity, the tool has no output schema and the description does not explain what the lint result looks like (e.g., a score, a list of issues). The user cannot infer the return format without additional context.
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?
Input schema has 100% coverage with clear descriptions. The description adds context for the vendor enum by specifying use cases, but adds no extra meaning for the path parameter beyond its schema 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 states 'Run lint for ONE specific ATS vendor only', with a clear verb and resource. It lists the allowed vendors and uses examples to distinguish from siblings like analyze_cv, making the purpose unambiguous.
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?
Explicitly provides when-to-use examples ('when the user asks something vendor-specific like...'). Does not explicitly state when not to use or name an alternative tool, though the sibling names imply a general CV analysis tool.
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.
v0.1.0analyze_cvget_express_urllint_for_vendorScored across 3 tools
The tools have distinct purposes: analyze_cv for all vendors, lint_for_vendor for a single vendor, and get_express_url for a visual URL. However, analyze_cv and lint_for_vendor both analyze CVs, which could cause minor confusion if descriptions are not read carefully.
Tool names follow a verb_noun pattern but with inconsistency: 'analyze_cv' and 'get_express_url' are direct, while 'lint_for_vendor' uses a preposition. The verb 'lint' is less standard than 'analyze'.
With 3 tools, the set is small but well-scoped for the domain of CV ATS analysis. It covers the essential operations without being overly sparse.
The tool set covers comprehensive analysis, vendor-specific linting, and a visual companion tool. Minor gaps like listing vendors or handling multiple files are absent but not critical for the core functionality.
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