# Careerjet MCP Server (`axlymxp/careerjet-mcp-server`) Actor

Query Careerjet jobs live from Claude, ChatGPT, Cursor and other AI agents via MCP. Search jobs, get counts, resolve locations and autocomplete keywords across 90+ countries — or run it as a classic scraper. Pay only per tool call.

- **URL**: https://apify.com/axlymxp/careerjet-mcp-server.md
- **Developed by:** [axly](https://apify.com/axlymxp) (community)
- **Categories:** Jobs, Agents, AI
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

Pay per event + usage

This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage.

Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

## Careerjet MCP Server

Give your AI assistant **live access to the global job market**. This is a
[Model Context Protocol](https://modelcontextprotocol.io) (MCP) server for
**[Careerjet](https://www.careerjet.com)** — a job-search aggregator indexing
millions of postings across **90+ countries** — so agents like **Claude, ChatGPT,
Cursor, and n8n** can search jobs, size demand, resolve locations, and suggest
keywords in real time.

It also runs as a **classic scraper**: start a normal run with the input schema
and it pushes normalized job rows to a dataset.

### Why AI teams use it

- **Recruiting copilots** — "Find remote data-engineer roles in Germany posted
  this week" answered live in chat.
- **Market-intel agents** — compare demand for a role across cities or countries
  with `count_jobs`.
- **Job-search assistants** — resolve a vague location, autocomplete keywords,
  then return matching openings.

### MCP tools

| Tool                    | What it does                                                  |
| ----------------------- | ------------------------------------------------------------- |
| `search_jobs`           | Search live jobs by keywords, location, country, and filters  |
| `count_jobs`            | Return the total number of matching jobs (market sizing)      |
| `resolve_location`      | Turn free-text into Careerjet's location tree with job counts |
| `autocomplete_keywords` | Suggest search keywords for a partial query                   |

Each job row includes title, company, salary, location, contract type, posting
date, and the full description.

### Connect it

1. Start the actor in **Standby mode** (it exposes an MCP endpoint).
2. Point your MCP client at `<ACTOR_STANDBY_URL>/mcp` (Streamable HTTP).
3. In Claude Desktop, Cursor, or your agent framework, add it as an MCP server —
   the four tools appear automatically.

#### Example tool call

```json
{
    "tool": "search_jobs",
    "arguments": {
        "keywords": "registered nurse",
        "location": "Texas",
        "country_code": "US",
        "sort": "date",
        "limit": 20
    }
}
```

### Classic scraper mode

Prefer a dataset? Run it normally with:

```json
{
    "keywords": "software developer",
    "location": "New York",
    "countryCode": "US",
    "maxItems": 100
}
```

Rows land in the dataset with the same fields, ready for JSON/CSV/Excel export or
Google Sheets, Make, and Zapier.

### Output fields

`job_id, title, company, company_logo_url, location, locations, salary,
contract_type, contract_period, is_new, posted_at, snippet, description_html,
description_text, url, country_code`.

### FAQ

**Which countries?** 90+ — pass `country_code` (US, GB, DE, FR, IN, AU, …).

**Do I need credentials?** No — Careerjet's job API is open. No proxy needed.

**How fresh is the data?** Pulled live at call time; use `sort: "date"` for the
newest postings.

**Is it reliable?** It calls Careerjet's own JSON API instead of scraping HTML, so
it doesn't break when the website changes.

**Can I still use it without MCP?** Yes — run it as a normal scraper via the input
schema.

# Actor input Schema

## `keywords` (type: `string`):

For classic (non-MCP) runs: job title, skill, or company to search.

## `location` (type: `string`):

For classic runs: city, region, or country. Empty = country-wide.

## `countryCode` (type: `string`):

Careerjet country site to search.

## `localeCode` (type: `string`):

Language\_COUNTRY locale (e.g. en\_US, fr\_FR).

## `sort` (type: `string`):

Result ordering for the classic run.

## `contractType` (type: `string`):

Filter by contract type (classic run).

## `contractPeriod` (type: `string`):

Full-time or part-time (classic run).

## `maxItems` (type: `integer`):

For classic (non-MCP) runs: stop after this many jobs.

## Actor input object example

```json
{
  "keywords": "software developer",
  "location": "New York",
  "countryCode": "US",
  "localeCode": "en_US",
  "sort": "relevance",
  "contractType": "",
  "contractPeriod": "",
  "maxItems": 50
}
```

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "keywords": "software developer",
    "location": "New York"
};

// Run the Actor and wait for it to finish
const run = await client.actor("axlymxp/careerjet-mcp-server").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "keywords": "software developer",
    "location": "New York",
}

# Run the Actor and wait for it to finish
run = client.actor("axlymxp/careerjet-mcp-server").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "keywords": "software developer",
  "location": "New York"
}' |
apify call axlymxp/careerjet-mcp-server --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "command": "npx",
            "args": [
                "mcp-remote",
                "https://mcp.apify.com/?tools=axlymxp/careerjet-mcp-server",
                "--header",
                "Authorization: Bearer <YOUR_API_TOKEN>"
            ]
        }
    }
}

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/acts/Al3tdeDjlwXoqpnVf/builds/7x6nb5R1VbUHU4Nrq/openapi.json