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marat-kabirov

mcp_server

by marat-kabirov
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AI Job Application Tracker Agent

一个智能体读取职位描述，通过 MCP 工具提取需求，将其与简历中的个人资料进行比较，输出带解释的 fit-score（go/no-go），并把决策记录到一个持久化的申请跟踪器中。

完整的技术范围 — 架构、MCP 工具契约、数据模式、逐日计划 — 在 SPEC.md 中。

状态

这是第 1 天完成后的项目骨架：仓库结构、Pydantic 数据模式和 MCP 服务器——其中注册了工具但尚未实现（每个工具都会抛出 NotImplementedError，并指出需要补写什么）。真正的提取/评分逻辑、LangGraph图、tracker DB 和 eval-数据集，是 SPEC.md 计划中的后续步骤。

Related MCP server: JobScannerMCP

设置

python -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"
cp .env.example .env  # заполнить ANTHROPIC_API_KEY

用你自己真实的技能填写 data/resume_profile.json（当前是带占位符的模板）——fit-score 的合理性取决于此文件。

验证 MCP 服务器能启动

python -m mcp_server.server

结构

job-tracker-agent/
├── SPEC.md                 # архитектура, контракты tools, план
├── mcp_server/
│   ├── server.py           # регистрация MCP tools (FastMCP)
│   ├── schemas.py          # Pydantic-модели
│   └── tools/
│       ├── extraction.py   # fetch_job_posting, extract_job_requirements
│       ├── scoring.py      # load_resume_profile, compute_fit_score
│       └── tracker.py      # log_application, update_application_outcome, query_tracker_stats
├── agent/
│   ├── state.py            # LangGraph state
│   └── graph.py            # узлы графа (заготовка)
├── data/
│   └── resume_profile.json # заполнить своими данными
└── evals/
    ├── golden_cases.yaml   # golden-кейсы для eval (заготовка)
    └── run_eval.py

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C
maintenance

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