@dontellu77
Career and workforce intelligence built on a deep HR ontology — skill taxonomies, role definitions and responsibilities, compensation and incentive structures, learning and development pathways, sourcing strategies, and role/skill evolution mapping. This structured foundation, co
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"careerproof": {
"url": "https://mcp.careerproof.ai/mcp"
}
}
}工具列表暂未提供
当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。
点击上方按钮,直接连接服务器端点实时获取工具列表。
Careerproof is an AI-powered career and workforce intelligence platform for professionals and organizations. It integrates with Claude Code and Gemini CLI via an MCP server, providing 42 tools and 6 slash-command skills for tasks like CV optimization, candidate evaluation, salary benchmarking, and workforce planning. The platform is powered by a curated RAG knowledge base from 50+ premium sources and live web research via Tavily.
Install the plugin in Claude Code using two commands: /plugin marketplace add dontellu77/careerproof-claude-skills then /plugin install careerproof-skills@careerproof. For Gemini CLI, install skills via a shell script and connect the MCP server with a bearer token. After authentication (OAuth for Claude Code, API key for others), invoke skills by name, e.g., /atlas-onboard or /ceevee-optimize, or use MCP tools directly in conversation.
Careerproof grounds its analysis in a curated RAG knowledge base from 50+ premium sources (HBR, McKinsey, BCG, Gartner, etc.) and live web research via Tavily, rather than stale training data. Its intelligence is calibrated to actual experience when a CV is provided, and it offers structured, research-backed outputs for both individual career decisions and organizational workforce strategy.
In Claude Code, OAuth is built in – on first use, a browser window opens for you to log in, and tokens are managed automatically. For Gemini CLI or programmatic access, you configure a bearer token (a `cpk_...` API key) in the MCP server configuration. Get your API key at careerproof.ai.
You need an active CareerProof account with available credits, plus either Claude Code or Gemini CLI. You can sign up at careerproof.ai.
Skill costs are listed in the README: `atlas-shortlist` 8–13 credits per candidate, `atlas-deep-eval` ~26 credits, `atlas-report` 15 credits, `ceevee-optimize` 10–13 credits, `ceevee-career-intel` 2 credits per message. The `atlas-onboard` skill is free.
Yes. When the MCP server is connected, you get direct access to all 42 tools in the conversation. For example, you can say "Upload this CV and run a competency analysis" and the model will use the appropriate tools.
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