jd-intel-mcp
Enables AI assistants to fetch and structure job postings from major ATS platforms (Greenhouse, Lever, Ashby, etc.) directly, eliminating copy-paste and context loss.
README
jd-intel
Stop pasting job descriptions into AI assistants. Let your AI fetch them directly.
Full text. Clean structure. Across every major ATS. No copy-paste. No context loss.
Why this exists
Your AI assistant already knows a lot about you. Your resume is in its memory. Your target roles, your past projects, your background. Ready to help the moment you feed it a job description.
So you copy-paste.
A JD from one company. Another from the next. A half-dozen more from your target list. Half have broken HTML. Salary info dies in translation. Links get stripped. And for every role, the dance starts over.
You could wait for the job boards to ship their own MCPs. They'll get there eventually. On their timeline. Filtered through their priorities, not yours. Tied to their query abstractions.
jd-intel skips that wait. Raw JDs, fetched directly by your AI, on your terms. One level below the curated layer.
Try asking your AI:
"Find AI/ML engineering jobs posted this week." "What product designer roles are open at fintechs right now?" "Pull the staff PM roles posted in the last 7 days."
Done.
Why not just scrape?
Because scraping breaks where jd-intel doesn't:
- Full JDs when browsing fails. SPA-rendered boards, slow loads, auth walls, and geo-restrictions block a browser. They don't block a public API call.
- Structured data, not HTML soup. Salary, location type, department, and clean markdown, normalized across every ATS.
- No keys, no browser. Public APIs only. Runs anywhere your AI does.
- One schema, every platform. Greenhouse, Lever, Ashby, SmartRecruiters, TeamTailor, Recruitee, Workday return the same shape.
What you can do with it
- Look up open roles at any company directly from your AI, no copy-paste
- Tailor your resume across ten roles in one conversation
- Rank openings by fit with your background
- Scan a whole sector: "Pull open roles at fintech companies posted this week"
- Research teams by reading their JDs in bulk
The toolkit fetches. Your AI thinks.
Install
Works with MCP-aware AI clients: Claude Desktop, Claude Code, Cursor, Windsurf. ChatGPT, Gemini, and other non-MCP clients don't support this yet. They use different tool-calling systems. (We wish they did. The protocol works the same way regardless of which AI you talk to.)
You'll need Node.js 18 or newer. To check: open a terminal and run node --version. If it's missing or older, install from nodejs.org first.
For Claude Desktop (one command)
-
Open a terminal. It's just a text window. Nothing destructive happens here.
- macOS: Spotlight (
⌘ Space), type "Terminal", hit Enter. - Windows: Start menu, type "PowerShell", hit Enter.
- macOS: Spotlight (
-
Paste this and hit Enter:
npx jd-intel-mcp install -
Quit and reopen Claude Desktop. The tools appear automatically.
Try: "Find product roles at devtools companies."
If something goes wrong or you'd rather edit the config file directly, see Manual install below.
For Cursor and Windsurf
These clients have their own MCP setup flows. Follow their docs:
- Cursor: docs.cursor.com
- Windsurf: docs.windsurf.com
Use this server config: command: "npx", args: ["-y", "jd-intel-mcp"].
For developers
npm install jd-intel
import { fetchJobs } from 'jd-intel';
const jobs = await fetchJobs({
company: '<your-target-company>',
titleFilter: 'designer',
postedWithinDays: 14,
limit: 50,
});
CLI usage: npx jd-intel fetch <company-slug> --title-filter "engineer" --posted-within-days 14. Full filter reference below.
Node.js 18+. No API keys. No configuration.
Manual install (fallback)
If npx jd-intel-mcp install fails, edit the config directly.
Config file location:
- macOS:
~/Library/Application Support/Claude/claude_desktop_config.json - Windows:
%APPDATA%\Claude\claude_desktop_config.json
{
"mcpServers": {
"jd-intel": {
"command": "npx",
"args": ["-y", "jd-intel-mcp"]
}
}
}
Restart Claude Desktop.
Updating
npx -y jd-intel-mcp auto-updates within ~24 hours via npm's cache. To force an update immediately:
npx clear-npx-cache
Then quit and reopen Claude Desktop.
If you installed the library or CLI directly:
npm install jd-intel@latest # force latest
# or
npm update jd-intel # respect semver
MCP tools
| Tool | Purpose |
|---|---|
fetch_jobs |
Get open roles at a company with filters for role type, topic, location, and recency |
search_registry |
Find companies by name or sector |
detect_ats |
Identify which ATS platform a company uses |
Plus one Resource: registry://jd-intel/all. Full company registry, grouped by ATS. Fetched lazily for broad catalog surveys.
What you get back
Every job normalizes to one schema, across every platform:
{
"id": "a1b2c3d4e5f6",
"company": "Example Co",
"title": "Senior Software Engineer, Platform",
"department": "Engineering",
"location": "Remote - US",
"locationType": "remote",
"salary": { "min": 180000, "max": 240000, "currency": "USD" },
"description": "Design and build the API surface our customers integrate against...",
"url": "https://boards.example.com/jobs/12345",
"postedAt": "2026-04-10T14:30:00Z"
}
No custom parsing per company.
Data model
| Field | Description |
|---|---|
title |
Full job title |
company |
Normalized company name |
department |
Team or department (when provided) |
location |
City, state, country, or remote |
locationType |
remote, hybrid, or onsite |
salary |
Min-max range with currency (when available) |
description |
Full JD in clean markdown |
url |
Direct link to the posting |
postedAt |
Publication date (when provided) |
Platforms supported
| Platform | Status | Typical use |
|---|---|---|
| Greenhouse | Shipped | Most widely used ATS in tech |
| Ashby | Shipped | Growing fast with startups |
| Lever | Shipped | Common at mid-stage companies |
| SmartRecruiters | Shipped | Enterprise and mid-market |
| TeamTailor | Shipped | European startups and scale-ups |
| Recruitee | Shipped | Dutch / EU SMBs and scale-ups |
| Workday | Shipped | Large enterprises (registry-keyed) |
| Personio | Planned | German / EU mid-market |
Adding a new ATS is a single adapter file. See Contributing.
Filters (quick reference)
| Flag | What it matches | Use for |
|---|---|---|
--title-filter |
Title only | Role identity (PM, engineer, designer) |
--filter |
Title + department + description | Topic or scope (integrations, growth) |
--posted-within-days |
Recent postings | Recency cuts |
--location-include |
Location contains any keyword | Region targeting |
--location-exclude |
Location contains no keyword | Drop geographic noise |
--limit |
First N results | Cap output size |
All filters AND together. Deep dive on patterns and gotchas: docs/filters.md.
Roadmap
Shipped
- Library, CLI, and MCP server (three surfaces of one toolkit)
- Greenhouse, Ashby, Lever, SmartRecruiters, TeamTailor, Recruitee, Workday adapters
- Title, topic, location, and date filters
- Salary extraction from JD text
- Verified company registry (160+ companies)
Next
- Personio adapter (German / EU mid-market)
- Anthropic MCP marketplace submission
Planned
- Workable adapter (parked — needs SPA shortcode resolution)
- Temporal tracking (when roles open, close, reopen)
- Change detection
- Resume-aware fit scoring
Contributing
Add a company to the registry: submit a PR to the appropriate file in registry/.
Add an ATS adapter: new file in src/adapters/. One adapter, one file. Follow the pattern of the existing adapters.
Request a company: open an issue. Tell me who's missing.
Built by
Prashant R. PM who builds. I try out and build what really matters below the AI hype.
- Portfolio and writing: prashantrana.xyz
License
MIT
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。
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。
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。
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。
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。
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。
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。
mcp-server-qdrant
这个仓库展示了如何为向量搜索引擎 Qdrant 创建一个 MCP (Managed Control Plane) 服务器的示例。