Build a weekly AI jobs digest agent in 20 lines of Python, no API key

Most job-search automation breaks on the same two things: scraping, and knowing which postings are worth reading. Level's catalog removes both. It is queryable over a free, read-only REST API and a remote MCP server, and every posting carries an AI score, so the "is this really an AI job" filter is a query parameter instead of a manual task. This tutorial builds a small digest script, shows where an agent fits on top of it, and lists the traps to watch for. Numbers were pulled on 16 October 2026.

The REST endpoint is https://jobsbylevel.com/api/v1/jobs. The parameters that matter for a digest are query, ai_level_min, ai_level_max, remote, city, company, posted_within_hours (1 to 168), sort=recent and page. Results come back 20 per page.

import json, urllib. parse, urllib. request BASE = " https://jobsbylevel.com/api/v1/jobs " def fetch ( ** params ): url = BASE + "? " + urllib. parse. urlencode ( params ) req = urllib. request. Request ( url, headers = { " User-Agent ": " weekly-digest/0.1 " }) with urllib. request. urlopen ( req, timeout = 30 ) as r: return json. load ( r ) data = fetch ( query = " machine learning ", ai_level_min = 3, remote = 1, posted_within_hours = 168, sort = " recent " ) print ( f " { data [ ' total ' ] } matching jobs in the last 7 days " ) for job in data [ " items " ][: 5 ]: print ( f " L { job [ ' ai_level ' ] } ( { job [ ' ai_score ' ] } ) { job [ ' title ' ] } @ { job [ ' company ' ] } " ) print ( " ", job [ " url " ]. split ( "?

" )[ 0 ]) Enter fullscreen mode Exit fullscreen mode I ran exactly this on 16 October 2026. It reported 110 matching jobs for the previous seven days. The first five were a Block security engineering role (score 65), a Databricks generative AI solutions architect role (70), a Natera Principal AI Engineer role (90), a Fetch staff machine learning engineer role (90) and an NVIDIA foundation model internship (90).

The first result is the lesson. A "Security Engineer" appeared for a "machine learning" query, because a search with a text query matches the posting text, not only the title. That is why the AI level filter exists: combined with ai_level_min=3, it keeps only postings where AI is central to the work, but you still want to look at the titles before you trust a keyword.

A digest is a loop with three decisions, and each maps to a field you already have:

Each result also carries a canonical url on the Level site. Keep that link when you show results to anyone; it is the one condition the data comes with.

If you already work inside Claude, Cursor or another MCP client, you do not need the script. Add the server, which is Streamable HTTP, stateless, read-only and needs no authentication:

claude mcp add --transport http level-jobs https://jobsbylevel.com/mcp Enter fullscreen mode Exit fullscreen mode The server exposes tools including search_jobs, latest_ai_jobs, get_job, market_stats, list_companies_by_ai_level and get_level_definitions. Ask your assistant for "remote Level 3 or 4 roles posted in the last 48 hours, grouped by company", and it will compose the calls itself.

A useful sanity check before you build anything is market_stats. On 16 October 2026 it reported 140,530 live postings, split 116,494 at Level 1, 5,446 at Level 2, 11,769 at Level 3 and 6,821 at Level 4, with a remote share of 17.7%. If your digest returns hundreds of results a week, you now know what fraction of the whole board that is.

Add city for a local digest, company to watch an employer, or ai_level_max=2 if you are looking for roles that use AI tools rather than build them. The parameters compose, and none of them need a signup.

Everything above, including the endpoint list and the fair-use limits, is documented on Level's free MCP server and REST API page.