Text Processing
比较简历与职位描述，归一化关键词变体并突出覆盖缺口
用三种语言从你的代码中调用此工具。
curl -X POST 'https://api.elysiatools.com/zh/api/tools/resume-job-description-matcher' \
-H 'Content-Type: application/json' \
-d '{"resumeText":"Skills: ReactJS, TypeScript, GraphQL","jobDescriptionText":"Need React.js, TypeScript, AWS, testing, and GraphQL experience","useAiSemanticMatch":true}'以 JSON 形式 POST 提交输入参数。文件类型参数需先单独上传。
POST https://api.elysiatools.com/zh/api/tools/resume-job-description-matcher| 参数名 | 类型 | 必填 | 说明 |
|---|---|---|---|
| resumeText | textarea | 是 | — |
| jobDescriptionText | textarea | 是 | — |
| useAiSemanticMatch | checkbox | 否 | — |
JSON 结果
{
"key": {...},
"metadata": {
"key": "value"
},
"error": "Error message (optional)",
"message": "Notification message (optional)"
}将此工具加入你的 Model Context Protocol 服务，让 AI 智能体可以列出并调用它。
将以下内容加入你的 MCP 客户端配置：
{
"mcpServers": {
"elysiatools-resume-job-description-matcher": {
"name": "resume-job-description-matcher",
"description": "比较简历与职位描述，归一化关键词变体并突出覆盖缺口",
"baseUrl": "https://api.elysiatools.com/mcp/sse?toolId=resume-job-description-matcher",
"command": "",
"args": [],
"env": {},
"isActive": true,
"type": "sse"
}
}
}连接到 SSE 端点后，列出已开放的工具：
{
"jsonrpc": "2.0",
"id": 1,
"method": "tools/list"
}通过工具 id 调用，参数由其参数表构建：
{
"jsonrpc": "2.0",
"id": 2,
"method": "tools/call",
"params": {
"name": "resume-job-description-matcher",
"arguments": {
"resumeText": "Skills: ReactJS, TypeScript, GraphQL",
"jobDescriptionText": "Need React.js, TypeScript, AWS, testing, and GraphQL experience",
"useAiSemanticMatch": true
}
}
}有问题或反馈？请联系 [email protected]