career-ops-mcp

A local MCP server that tracks a job-search and freelance-lead pipeline, and drafts follow-up messages from the notes logged against each item. Built as a portfolio piece to demonstrate a repeatable, AI-augmented way of working: Define → Discover → Draft → Validate → Deliver, applied to a real personal-ops problem rather than a toy example.

Why this exists: most "AI portfolio projects" are a chatbot wrapper. This is a small, working tool with real state (a JSON-backed pipeline), a real generative step (drafting a follow-up from stored context, not from scratch), and a real interface (MCP, so it plugs into Claude Desktop or Claude Code like any other tool) — it's operational, not a demo.

What it does

Exposes five tools over MCP:

Data is stored locally in data/pipeline.json — nothing leaves your machine except the one draft_followup call, which hits the Anthropic API.

Related MCP server: jobfinder-mcp

Setup

git clone <this-repo>
cd career-ops-mcp
pip install -r requirements.txt
cp .env.example .env   # then add your ANTHROPIC_API_KEY

Run it

python server.py

This starts the server over stdio, which is how MCP clients expect to talk to it — it isn't meant to be run standalone and left open in a terminal for browsing.

Connect it to Claude Desktop

Add this to your claude_desktop_config.json (Settings → Developer → Edit Config):

{
  "mcpServers": {
    "career-ops": {
      "command": "python",
      "args": ["/absolute/path/to/career-ops-mcp/server.py"]
    }
  }
}

Restart Claude Desktop, and you'll be able to say things like "add Acme Corp as a job application, stage applied" or "draft a follow-up for Northwind Consulting" directly in chat.

Connect it to Claude Code

claude mcp add career-ops -- python /absolute/path/to/career-ops-mcp/server.py

Project layout

career-ops-mcp/
├── server.py      # MCP tool definitions (the interface)
├── storage.py      # JSON-backed CRUD for pipeline items (the state)
├── ai.py            # Anthropic API call for draft_followup (the AI step)
├── data/            # pipeline.json lives here at runtime (gitignored)
├── requirements.txt
└── .env.example

Notes

• Single-user, single-process, local file storage — deliberately not built for concurrency. This is a personal tool, not a service.

• draft_followup never invents facts: the prompt explicitly constrains it to the notes already logged against that item, so the "AI step" is drafting from real context rather than generating generic filler.