Your AI assistant can run your job search now

Somewhere in the last year, the question changed. It used to be “which job board should I check?” Now, for a growing number of serious candidates, it’s “what did my assistant find overnight?”

Jobably was built for the second question. It is, as far as we know, the first career platform designed agent-native: every capability the web app has — searching, scoring, tailoring, pipeline tracking — is also a tool your AI assistant can drive directly, through a full MCP surface. Not a chatbot bolted onto a product. The product itself, operable by an agent.

What that looks like in practice

You connect Jobably to your assistant once — Claude, or anything else that speaks MCP — with a personal access token you mint in Settings. From then on, conversations like these work against your live pipeline:

“What came in overnight worth my time?” Your assistant reads the new matches with their scores and the written rationale behind each number, and answers with judgment: two worth a look, one borderline, here’s why.

“Tailor my resume for the Shapeways role.” The tailoring engine runs — the same one behind the web app, with the same provenance guard — and the docx lands in your pipeline.

“Where did I get to with Acme?” Status history, notes, and the follow-up clock, read straight from the board.

The difference between this and pasting a job description into a chat window is the difference between a colleague with access to your files and a stranger with a good memory. Your assistant isn’t improvising from what you told it — it’s reading your rubric, your verified history, and your actual pipeline.

Reading is free. Acting is deliberate.

Handing an agent the keys to something that spends money and sends documents raises an obvious question, so we made the answer structural:

• Every tool declares its cost class in its own description — an agent knows before calling whether a tool reads, spends, or destroys.
• Anything that spends or changes state requires explicit confirmation. Reading your matches is free; triggering a tailoring run is a deliberate, confirmed act.
• Scopes are enforced server-side. A read-only token cannot mutate your pipeline no matter what the agent asks for.

The same trust posture runs through the output itself. Scores come with written rationales, never bare numbers. Tailored resumes pass a provenance guard — a figure that doesn’t exist verbatim in your own history kills the draft in code, not in a promise. Outreach is draft-only by design: the system prepares, you send.

Discoverable by any agent, not just configured ones

Agent-native also means the platform announces itself the way agents discover services: an OpenAPI description of the whole surface, a /.well-known catalog, an MCP server card, and llms.txt. If your tooling can read a spec, it can find and operate Jobably without a human writing glue code first.

Why this matters for a serious search

A senior search is a monitoring problem punctuated by short bursts of intense work. The monitoring — scanning sources on a schedule, scoring what arrives, noticing that a company you interviewed with went quiet — is exactly what software should do. The bursts — deciding, preparing, showing up sharp — are yours.

An agent-native platform puts your assistant on the monitoring side with you: standing questions (“tell me when anything scores 9”), morning briefings, prep on demand. Jobably supplies the system underneath — the scans, the rubric, the guarded tailoring, the board — so the conversation has something real to stand on.

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