JobDataLake MCP Server
JobDataLake MCP 服务器
直接从 Claude、Cursor、Windsurf 或任何兼容 MCP 的 AI 工具中搜索 1,000,000 多条丰富的职位列表,涵盖 20,000 多家公司。
免费使用 — 无需注册。 包含每天 500 次调用额度。
快速开始
将其添加到您的 Claude Code 或 Claude Desktop MCP 配置中:
{
"mcpServers": {
"jobdatalake": {
"command": "npx",
"args": ["-y", "@jobdatalake/mcp-server"]
}
}
}就是这样。无需 API 密钥。然后您可以询问:
“帮我找薪资超过 15 万美元的远程高级 React 职位”
“本周发布的入门级数据科学职位”
“Anthropic 公司薪资超过 20 万美元的职位”
想要无限访问权限?
在 jobdatalake.com 注册以获取您自己的 API 密钥,然后添加它:
{
"mcpServers": {
"jobdatalake": {
"command": "npx",
"args": ["-y", "@jobdatalake/mcp-server"],
"env": {
"JDL_API_KEY": "jdl_your_key_here"
}
}
}
}Related MCP server: JobSpy Cowork MCP Server
工具
search_jobs
通过关键词、技能、薪资、远程类型、资历、地点、日期等搜索和筛选职位。
筛选器:
query— 关键词搜索(职位名称、公司、技能)skills— AND 筛选:Python,AWS,Kubernetes(必须全部匹配)salary_min/salary_max— 以美元为单位(接受完整金额,例如 150000)remote_type—fully_remote(全远程)、hybrid(混合办公)、on_site(现场办公)seniority—Entry(入门级)、Mid Level(中级)、Senior(高级)、Staff(资深)、Principal(首席)、Manager(经理)、Director(总监)、C Level(高管)location— 城市、国家或大洲(Europe、Asia、Latin America、Scandinavia等)countries— ISO 代码:US,GB,DEjob_function—eng(工程)、data(数据)、design(设计)、sales(销售)、marketing(市场)、product(产品)等employment_type—full_time(全职)、part_time(兼职)、contract(合同工)、internship(实习)posted_within—24h、7d、30dsort_by—posted_at:desc、salary_max_usd:desc、salary_min_usd:asccompany— 按域名筛选:stripe.com
get_job
获取特定职位的详细信息,包括描述、要求、薪资和申请链接。请使用搜索结果中的 job_handle ID。
get_company
获取公司概况 — 行业、规模、融资情况、职业页面。接受域名(stripe.com)或 handle。
get_filter_options
发现可用的筛选值及其职位数量。非常适合探索数据集中的内容。
find_similar_jobs
使用 AI 向量相似度查找与给定职位相似的职位。适用于远程和技术类职位。
定价
免费层级(无需注册): 每天 500 次调用,每日重置。
使用您自己的 API 密钥:
免费:注册即送 1,000 积分
入门版:1,000,000 积分 — 200 美元
成长版:2,000,000 积分 — 300 美元
商业版:4,000,000 积分 — 400 美元
积分永不过期。获取您的 API 密钥。
数据
来自 20,000 多家公司的 100 万+ 活跃职位列表
40+ ATS 平台(Greenhouse、Lever、Workday、Ashby 等)
每小时更新
AI 增强:薪资(美元)、技能、资历、远程政策、职位职能
搜索响应时间低于 100 毫秒
查询示例
查询 | 功能 |
“Remote Python jobs over $150k” | 技能 + 薪资 + 远程筛选 |
“Jobs at Stripe” | 公司筛选 |
“Entry level data science” | 资历筛选 |
“New remote jobs today” | 日期 + 远程筛选 |
“React AND TypeScript jobs in Europe” | 多技能 AND + 大洲筛选 |
“Highest paying remote jobs” | 薪资排序 |
“What skills are most in demand?” | 筛选选项工具 |
许可证
MIT — 参见 LICENSE。
支持
Available Tools
4 toolsfind_similar_jobsA
Find jobs similar to a given job listing using AI vector similarity. Great for "more like this" discovery.
Inputs: job_id (required), per_page.
get_companyB
Get company profile including open job count, industry, size, and career page URL.
Inputs: company (required).
get_jobC
Get full details for a specific job listing including description, requirements, salary, and apply link.
Inputs: job_id (required).
search_jobsB
Search 1M+ job listings from 20K+ companies. Supports keyword search, AI semantic search, filters for location, salary, remote type, seniority, skills, and more.
Inputs: query, semantic_query, location, remote_type, +10 more.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 4 tools
Each tool has a clearly distinct purpose: find_similar_jobs focuses on AI-based similarity matching for job discovery, get_company retrieves company profiles, get_job fetches specific job details, and search_jobs handles broad keyword/semantic search with filtering. There is no overlap in functionality, making tool selection unambiguous.
All tool names follow a consistent verb_noun pattern with snake_case (e.g., find_similar_jobs, get_company, get_job, search_jobs). The verbs (find, get, search) are appropriately descriptive and maintain a predictable naming convention throughout the set.
With 4 tools, the server is well-scoped for job data exploration, covering key operations like searching, retrieving details, finding similar items, and company profiling. It feels slightly lean but reasonable, as core workflows are supported without unnecessary bloat.
The toolset provides comprehensive coverage for job data querying and discovery, including search, detailed retrieval, similarity matching, and company context. Minor gaps exist, such as no explicit update or delete operations, but these are likely unnecessary for a read-only data lake, and agents can work effectively with the available tools.
Maintenance
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