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marat-kabirov

mcp_server

by marat-kabirov
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Python
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AI Job Application Tracker

求人情報を読み、MCP toolsを介して要件を抽出し、それらを履歴書プロフィールと比較して、説明付きのfit-score（go/no-go）を出力し、判断を永続的な応募トラッカーに記録するエージェントです。

完全な技術スコープ（アーキテクチャ、MCP toolsの契約、データスキーマ、日別プラン）は SPEC.md にあります。

ステータス

これは1日目の時点でのプロジェクトのスケルトンです。リポジトリ構造、Pydanticのデータスキーマ、および登録済みだが未実装のツールを持つMCPサーバーが含まれます（各ツールは、何を追記すべきかを示す NotImplementedError を呼び出します）。実際の抽出/スコアリングロジック、LangGraphグラフ、tracker DB、evalデータセットは、SPEC.mdの計画に沿った次のステップです。

Related MCP server: JobScannerMCP

セットアップ

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

data/resume_profile.json を実際の自分のスキルで埋めてください（現在はプレースホルダー付きのテンプレートです）。fit-scoreがどれだけ適切になるかは、これに依存します。

MCPサーバーの起動確認

python -m mcp_server.server

構造

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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