career-scout-mcp
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@career-scout-mcpscore my resume for senior data scientist roles"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
career-scout-mcp
A production-grade Model Context Protocol (MCP) server demonstrating the wrapping pattern for AI-augmented data pipelines. Built as a standalone artifact: one LXC container, one Cloudflare Tunnel, one repo. Self-hosted via Ollama + LiteLLM SDK.
This server demonstrates the pattern I would apply to wrap Career Scout — my private job-search scoring pipeline. Synthetic data committed here for portability and reproducibility.
Documentation
Full architecture and design decisions: career-scout-mcp.stojadinovic.at
Stack
Python 3.13 (mypy strict)
MCP SDK with decorator-based primitive registration
LiteLLM SDK — provider-agnostic LLM routing, model-swappable via env
Ollama + Qwen 2.5 3B (default) — self-hosted, biomedical-research-portable
Pydantic for config + tool schemas
loguru structured JSON logging with secret redaction
Debian 13 LXC, cloudflared edge termination, nginx static docs
Prerequisites
Python 3.13 (uv manages this automatically)
uv — dependency and environment management
Ollama — default local LLM provider for
qwen2.5:3b
Memory: Ollama's headroom calc for qwen2.5:3b requires ~6 GiB of available memory (it counts buff/cache as unavailable). A 4 GiB system may fail to load the model even though it's 1.9 GB on disk.
Debian 13
sudo apt-get update && sudo apt-get install -y curl ca-certificates zstd
curl -LsSf https://astral.sh/uv/install.sh | sh
curl -fsSL https://ollama.com/install.sh | sh
ollama pull qwen2.5:3bNote:
zstdis required by the Ollama installer for archive extraction on minimal Debian; not all base images include it.
macOS
brew install uv ollama
ollama serve &
ollama pull qwen2.5:3bWindows
uv installer · Ollama installer, then ollama pull qwen2.5:3b.
Quick start (local stdio)
uv sync
uv run python -m career_scout_mcpThe server exposes 4 tools, 5 resources (6 URIs), and 2 prompts via stdio. Connect from Claude Desktop, Claude Code, or OpenCode by pointing them at this binary.
Try it out
The fastest way to exercise the server is via MCP Inspector:
npx @modelcontextprotocol/inspector uv run python -m career_scout_mcpOpens a browser UI at localhost:6274 where you can list resources, render prompts, and invoke tools end-to-end against your local Ollama.
Development
Dev workflow uses OpenCode + standard Python tooling. See CONTRIBUTING.md.
Security
See SECURITY.md for reporting. Key posture:
All SQL parameterized (never f-string)
Pydantic input validation on every tool entry
Path traversal prevention on resource URIs
systemd hardening (non-root, ProtectSystem=strict, etc.)
MCP server NEVER publicly exposed (stdio default, HTTP bound 127.0.0.1 only)
TLS via Cloudflare edge — no local cert management surface
Docs deploy via manual
scripts/deploy_docs.sh. MCP server is never publicly exposed — stdio default; HTTP transport loopback-only behind Bearer auth (hmac.compare_digest).
License
MIT — see LICENSE.
Built by Stefan Stojadinovic, Vienna. Contact: stefan@stojadinovic.at
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