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benediktgirz

StoryLenses MCP Server

by benediktgirz

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

storylenses_analyze_job

从职位发布中提取 15 个以上的结构化字段

storylenses_match_profile

将候选人简历与职位数据进行匹配

storylenses_generate_letter

生成故事驱动的求职信

storylenses_quality_check

对求职信进行评分和评估

storylenses_list_archetypes

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

STORYLENSES_API_KEY

您从 storylenses.app/mcp 获取的 API 密钥

STORYLENSES_API_URL

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.

  1. 5 tool updatesv0.1.0
    • First observedstorylenses_analyze_job
    • First observedstorylenses_generate_letter
    • First observedstorylenses_list_archetypes
    • First observedstorylenses_match_profile
    • First observedstorylenses_quality_check

TDQS

A3.8/5.0

Scored across 5 tools

Disambiguation5/5

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.

Naming Consistency5/5

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.

Tool Count5/5

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.

Completeness4/5

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

ActivityInactive
ResponsivenessNo issues

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