career-scout-mcp
A production-grade MCP server demonstrating the wrapping pattern for AI-augmented data pipelines, specifically a job-search scoring pipeline.
README
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 qwen2.5:3b
Note:
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 qwen2.5:3b
Windows
uv installer · Ollama installer, then ollama pull qwen2.5:3b.
Quick start (local stdio)
uv sync
uv run python -m career_scout_mcp
The 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_mcp
Opens 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
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。