StoryLenses MCP Server
StoryLenses MCP 服务器
面向 MCP 兼容智能体的 AI 驱动求职信生成工具。这是首个用于求职申请的生产级 MCP 服务器。
工具
工具 | 描述 |
| 从职位发布中提取 15 个以上的结构化字段 |
| 将候选人简历与职位数据进行匹配 |
| 生成故事驱动的求职信 |
| 对求职信进行评分和评估 |
| 列出可用的叙事原型和语气 |
Related MCP server: laddro-career-mcp
设置
Claude Desktop
添加到 ~/Library/Application Support/Claude/claude_desktop_config.json:
{
"mcpServers": {
"storylenses": {
"command": "npx",
"args": ["-y", "@storylenses/mcp-server"],
"env": {
"STORYLENSES_API_KEY": "your-api-key"
}
}
}
}Cursor / VS Code
添加到 .cursor/mcp.json 或 VS Code MCP 设置中:
{
"mcp": {
"servers": {
"storylenses": {
"command": "npx",
"args": ["-y", "@storylenses/mcp-server"],
"env": {
"STORYLENSES_API_KEY": "your-api-key"
}
}
}
}
}Docker
docker run -e STORYLENSES_API_KEY=your-key ghcr.io/benediktgirz/storylenses-mcp-server环境变量
变量 | 必需 | 描述 |
| 是 | 您从 storylenses.app/mcp 获取的 API 密钥 |
| 否 | API 基础 URL(默认:https://www.storylenses.app) |
工作流示例
// 1. Analyze job posting
const job = await callTool("storylenses_analyze_job", {
job_url: "https://linkedin.com/jobs/view/12345"
});
// 2. Match candidate profile
const match = await callTool("storylenses_match_profile", {
job_analysis: job,
candidate_cv: "Senior engineer with 7 years React experience..."
});
// 3. Generate cover letter
const letter = await callTool("storylenses_generate_letter", {
job_analysis: job,
match_data: match,
candidate_name: "Alex Chen",
archetype: match.suggestedArchetype
});
// 4. Quality check
const score = await callTool("storylenses_quality_check", {
letter_text: letter.letter_text,
job_analysis: job
});测试
npm install
STORYLENSES_API_KEY=your-key npm test获取 API 密钥
访问 storylenses.app/mcp 获取您的 API 密钥。
免费版:每月 10 次生成
开发者版($29/月):200 次生成
规模版($99/月):1,000 次生成
许可证
MIT
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
5 tool updates
v0.1.0- First observed
storylenses_analyze_job - First observed
storylenses_generate_letter - First observed
storylenses_list_archetypes - First observed
storylenses_match_profile - First observed
storylenses_quality_check
TDQS
Scored across 5 tools
Each tool occupies a distinct stage in the workflow pipeline: analyze_job extracts job data, match_profile compares candidate to job, list_archetypes provides style options, generate_letter creates output, and quality_check validates results. No functional overlap exists between tools.
All tools follow an identical storylenses_verb_noun pattern with consistent snake_case formatting, clear action-oriented verbs (analyze, generate, list, match, check), and uniform prefixing that prevents collisions with other servers.
Five tools is ideally suited for this focused domain, covering the complete cover letter lifecycle from input analysis and matching through generation and validation without bloat, redundancies, or missing critical steps.
Provides comprehensive coverage of the generation workflow including analysis, matching, archetype selection, creation, and scoring; minor gap requires regenerating from scratch rather than refining existing letters based on quality feedback.
Maintenance
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