jobpipe

An MCP server that lets Claude run a job-search pipeline, where the model's judgement is checked by code before it is allowed to count.

Claude does the one thing a model is good at here: reading a job description and judging fit. Everything else (sourcing, de-duplication, visa-sponsor checks, hard eligibility rules, storage, calibration) is plain, testable Python. And the model's scores are not trusted on faith: every score must quote the job description, and the server verifies the quote exists before the score counts.

It reads only public, unauthenticated sources. No LinkedIn scraping, no session cookies, no form submission.

Why this is interesting beyond job hunting

The pattern is general: use an LLM for judgement, and wrap it in deterministic controls that make the judgement auditable. It is the same idea I apply professionally when validating AI systems in financial services.

Related MCP server: job-search-mcp

How it works

flowchart LR
    A["Greenhouse / Lever / Ashby<br/>public JSON boards"] --> B[Normalise + dedupe]
    B --> C["UK sponsor register<br/>(GOV.UK CSV)"]
    C --> D["Deterministic rules<br/>no model"]
    D -->|REJECT, with reason| X[(kept for audit)]
    D -->|PASS / REVIEW| E["Claude scores<br/>against rubric"]
    E --> F{"Evidence check<br/>quote in JD?"}
    F -->|no| G[dimension voided]
    F -->|yes| H[score counts]
    G --> I[Band: AUTO_DRAFT / REVIEW / REJECT]
    H --> I
    I --> J["Your labels → precision / recall"]

The evidence check, concretely

The model returns, for each dimension, a score and a quote:

{"job_id": "greenhouse:examplecapital:123",
 "dimensions": {
   "quant_depth": {"score": 4, "abstain": false,
                   "evidence": "backtesting and benchmarking of pricing models"},
   "...": "one entry per rubric dimension"}}

record_assessment normalises typography and whitespace, then confirms the quote occurs in the stored description (a 95% fuzzy floor tolerates curly quotes and line breaks, nothing looser). If it does not, the dimension is voided and the job cannot reach AUTO_DRAFT however high its total. Fabricating evidence therefore lowers the score instead of being ignored. That is the difference between a prompt that asks for citations and a system that enforces them.

Live check (15 October 2026)

The parsers were run against three real public boards, one per ATS, using examples/live_check.py:

Deterministic pre-filter on those 499 (sponsor register not consulted in this check):

The security-clearance rule fired 181 times (171 Palantir, 10 Cohere). Spot checks confirmed these were genuine clearance requirements, not keyword noise. So before a single token is spent, the model's workload drops from 499 postings to the 283 that are actually possible.

Tools exposed to Claude

Typical session:

refresh_sponsor_register
fetch_jobs
check_sponsorship        (companies from the new jobs)
run_prefilter
next_to_score → get_rubric → score each → record_assessment
list_pipeline band=AUTO_DRAFT

Quick start

git clone https://github.com/Jayaramgokul/jobpipe.git && cd jobpipe
python3 -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
cp config.example.yaml config.yaml      # then list the boards you care about
python -m pytest tests/ -q              # 30 offline tests
python examples/live_check.py           # optional: hits three live boards

Connect it to Claude

Claude Code / Claude Desktop (local, stdio):

claude mcp add jobpipe -- /full/path/to/.venv/bin/python -m jobpipe.server

or in claude_desktop_config.json:

{
  "mcpServers": {
    "jobpipe": {
      "command": "/full/path/to/.venv/bin/python",
      "args": ["-m", "jobpipe.server"],
      "env": {
        "JOBPIPE_CONFIG": "/full/path/to/config.yaml",
        "JOBPIPE_DATA": "/full/path/to/data"
      }
    }
  }
}

As a remote connector: set JOBPIPE_TRANSPORT=streamable-http and run it behind a host that terminates TLS. A remote connector is reached from the cloud, so localhost will not work. Local stdio is the better default, because the pipeline data is personal.

Project layout

jobpipe/
  ats.py        fetch + normalise Greenhouse, Lever, Ashby; dedupe
  sponsors.py   GOV.UK sponsor register: download, cache, fuzzy match
  prefilter.py  deterministic HARD / SOFT rules with evidence snippets
  rubric.py     rubric, JSON schema, evidence verification, banding
  store.py      SQLite state, labels, calibration metrics
  server.py     MCP server exposing the 11 tools
tests/          30 offline tests (fixtures, no network)
examples/       live smoke test against real boards

Calibrate before trusting it

The weights and thresholds shipped here are a starting guess, not a measurement. Label 40 to 60 postings you have already judged by eye, then run calibration_report. Read precision and recall separately because they cost different things: a false positive wastes a tailoring cycle, a false negative loses a job you wanted. If AUTO_DRAFT precision is below about 0.85, tighten the weights or raise the threshold. If recall is below about 0.7, the threshold is too high.

Limitations, stated plainly

• Workday is not covered. It has no public JSON feed, which excludes most of the Big Four and most large banks. Use email alerts for those.

• Company-name matching is genuinely hard. The register holds legal entity names; boards carry brand names. AMBIGUOUS is returned deliberately rather than guessing from the top fuzzy score.

• Register freshness matters. Ratings change without notice. Force a refresh before acting on anything important.

• Being on the register is not an offer of sponsorship. It means the employer can sponsor, not that it will for a given role.

• Board slugs rot. Companies migrate ATS and old boards 404 quietly; fetch_jobs reports these under board_errors.

• The rubric is tuned to one profile (UK model risk and AI governance). The dimensions live in rubric.py and are meant to be edited.

Licence

MIT