# 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 a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. 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. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/integrations/mcp.md). If your project is in a different language, use 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 `/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 '' with your token const client = new ApifyClient({ 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 '' with your token. client = ApifyClient("") # 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 " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Careerjet MCP Server", "description": "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.", "version": "1.0", "x-build-id": "7x6nb5R1VbUHU4Nrq" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/axlymxp~careerjet-mcp-server/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-axlymxp-careerjet-mcp-server", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/axlymxp~careerjet-mcp-server/runs": { "post": { "operationId": "runs-sync-axlymxp-careerjet-mcp-server", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/axlymxp~careerjet-mcp-server/run-sync": { "post": { "operationId": "run-sync-axlymxp-careerjet-mcp-server", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "keywords": { "title": "Keywords", "type": "string", "description": "For classic (non-MCP) runs: job title, skill, or company to search." }, "location": { "title": "Location", "type": "string", "description": "For classic runs: city, region, or country. Empty = country-wide." }, "countryCode": { "title": "Country index", "enum": [ "US", "GB", "CA", "AU", "IE", "IN", "DE", "FR", "ES", "IT", "NL", "BE", "CH", "AT", "SE", "NO", "DK", "FI", "PL", "PT", "BR", "MX", "AR", "ZA", "SG", "MY", "PH", "NZ", "AE", "JP" ], "type": "string", "description": "Careerjet country site to search.", "default": "US" }, "localeCode": { "title": "Locale", "type": "string", "description": "Language_COUNTRY locale (e.g. en_US, fr_FR).", "default": "en_US" }, "sort": { "title": "Sort order", "enum": [ "relevance", "date", "salary" ], "type": "string", "description": "Result ordering for the classic run.", "default": "relevance" }, "contractType": { "title": "Contract type", "enum": [ "", "p", "c", "t", "i", "a" ], "type": "string", "description": "Filter by contract type (classic run).", "default": "" }, "contractPeriod": { "title": "Working hours", "enum": [ "", "f", "p" ], "type": "string", "description": "Full-time or part-time (classic run).", "default": "" }, "maxItems": { "title": "Max results (classic run)", "minimum": 1, "type": "integer", "description": "For classic (non-MCP) runs: stop after this many jobs.", "default": 50 } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```