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marat-kabirov

mcp_server

by marat-kabirov
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Python
Local

AI Job Application Tracker Agent

에이전트가 채용 공고를 읽고, MCP 도구를 통해 요구 사항을 추출하며, 이를 이력서 프로필과 비교하여 설명과 함께 fit-score(go/no-go을 산출하고, 그 결정을 영구적인 지원 추적에 기록합니다.

전체 технический scope — архитектура, контракт MCP tools, данные схемы, план по д нижам — in SPEC.md.

Status

This is the project skeleton after day 1: repository structure, Pydant-intic data schemas, МCP server with registered but not yet implemented tools (каждый tool call NotImplementedError and says that something needs to be rewritten). Fактуальная logicка extraction/скоринга, LапGraph-граф, tracking DB и eval-нбор — следующие шаги по плану SPEC.

Related MCP server: JobScannerMCP

Setup

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

data/resume_profile.json data with the real skills of the user is expected. The, this is a стереотип with пласихолдераим. Ot go зависит након естень адекватно fit-score.

MCP сервер запускается

python -m mcp_server.server

Structure

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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