What is the LAPRAS_API_KEY for?
The API key is required only for tools that read or modify career data (experiences, summaries, skills, goals). It is not needed for job searching.
@lapras-inc
基本信息
lapras.com 公式MCP Server
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"lapras": {
"command": "npx",
"args": [
"-y",
"@lapras-inc/lapras-mcp-server"
],
"env": {
"LAPRAS_API_KEY": "<YOUR_LAPRAS_API_KEY>"
}
}
}
}Search job by keyword, position, and minimum annual salary
Get detailed information about a specific job posting. You can apply for jobs through the URL provided in the response.
Get work experiences on LAPRAS(https://lapras.com)
Create a new work experience on LAPRAS(https://lapras.com). You can check the result at https://lapras.com/cv
Update a work experience on LAPRAS(https://lapras.com). You can check the result at https://lapras.com/cv
Delete a work experience from LAPRAS(https://lapras.com). You can check the result at https://lapras.com/cv
Get job summary(職務要約) on LAPRAS(https://lapras.com)
Update job summary(職務要約) on LAPRAS(https://lapras.com). You can check the result at https://lapras.com/cv
Get career aspirations(今後のキャリアでやりたいこと) on LAPRAS(https://lapras.com)
Update career aspirations(今後のキャリアでやりたいこと) on LAPRAS(https://lapras.com). You can check the result at https://lapras.com/cv
Get current tech skills(経験技術・スキル・資格) on LAPRAS(https://lapras.com)
Update tech skills(経験技術・スキル・資格) on LAPRAS(https://lapras.com)
LAPRAS MCP Server is the official Model Context Protocol server for the LAPRAS career platform (lapras.com). It enables AI assistants to search for jobs and manage career data—such as work history, job summaries, career goals, and tech skills—stored in LAPRAS.
Install via npx, Docker, or a Desktop Extension (.dxt file). Add the server configuration to your MCP client’s settings (e.g., mcp.json or claude_desktop_config.json). For tools that modify career data, set LAPRAS_API_KEY (obtained from https://lapras.com/config/api-key). The server also works with Google’s Gemini CLI using the same JSON config (or TOML for gemini.conf.toml).
The API key is required only for tools that read or modify career data (experiences, summaries, skills, goals). It is not needed for job searching.
You can generate one from your LAPRAS account settings: https://lapras.com/config/api-key.
If the npx method fails (e.g., due to Node.js environment issues), use the Docker version instead. On WSL, you need to pass environment variables directly in the command arguments.
Yes. Any data retrieved from LAPRAS (including personal information) is sent to the AI model for processing. Review the AI service’s data handling policies before using sensitive information.
The server uses MCP (Model Context Protocol) over stdio. Authentication is handled via the LAPRAS_API_KEY environment variable set in the client configuration.
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